Overview

Dataset statistics

Number of variables12
Number of observations166
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.3 KiB
Average record size in memory106.8 B

Variable types

Categorical1
Text1
Numeric10

Dataset

Description조사기준일까지 해외 육성자가 육성한 신품종이 우리나라에 품종보호 출원되어 등록된 품종수를 작물(식물)별로 연도별로 정리한 내역
URLhttps://www.data.go.kr/data/15012695/fileData.do

Alerts

작 물 명 has unique valuesUnique
1998-2013 has 115 (69.3%) zerosZeros
2014 has 151 (91.0%) zerosZeros
2015 has 141 (84.9%) zerosZeros
2016 has 143 (86.1%) zerosZeros
2017 has 147 (88.6%) zerosZeros
2018 has 140 (84.3%) zerosZeros
2019 has 144 (86.7%) zerosZeros
2020 has 144 (86.7%) zerosZeros
2021 has 141 (84.9%) zerosZeros
2022 has 142 (85.5%) zerosZeros

Reproduction

Analysis started2023-12-12 10:20:32.183420
Analysis finished2023-12-12 10:20:43.876924
Duration11.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

작물분류
Categorical

Distinct7
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
화훼류
110 
과수류
29 
채소류
14 
식량작물
 
5
버섯류
 
4
Other values (2)
 
4

Length

Max length4
Median length3
Mean length3.0542169
Min length3

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row식량작물
2nd row식량작물
3rd row식량작물
4th row식량작물
5th row식량작물

Common Values

ValueCountFrequency (%)
화훼류 110
66.3%
과수류 29
 
17.5%
채소류 14
 
8.4%
식량작물 5
 
3.0%
버섯류 4
 
2.4%
특용작물 3
 
1.8%
사료작물 1
 
0.6%

Length

2023-12-12T19:20:43.956188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:20:44.118777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
화훼류 110
66.3%
과수류 29
 
17.5%
채소류 14
 
8.4%
식량작물 5
 
3.0%
버섯류 4
 
2.4%
특용작물 3
 
1.8%
사료작물 1
 
0.6%

작 물 명
Text

UNIQUE 

Distinct166
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-12T19:20:44.408027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length5.7228916
Min length1

Characters and Unicode

Total characters950
Distinct characters252
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique166 ?
Unique (%)100.0%

Sample

1st row고구마
2nd row메밀
3rd row벼
4th row옥수수
5th row콩
ValueCountFrequency (%)
x 5
 
2.6%
체리(감과양앵두 3
 
1.6%
필라 3
 
1.6%
드라세나 3
 
1.6%
노디플로라 2
 
1.1%
프루누스세라서스 2
 
1.1%
류코토이 2
 
1.1%
안개초 2
 
1.1%
알스트로메리아속 1
 
0.5%
아르기란테뭄 1
 
0.5%
Other values (166) 166
87.4%
2023-12-12T19:20:44.855405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 44
 
4.6%
리 36
 
3.8%
라 36
 
3.8%
아 30
 
3.2%
이 26
 
2.7%
24
 
2.5%
( 20
 
2.1%
) 20
 
2.1%
나 18
 
1.9%
속 16
 
1.7%
Other values (242) 680
71.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 877
92.3%
Space Separator 24
 
2.5%
Open Punctuation 20
 
2.1%
Close Punctuation 20
 
2.1%
Uppercase Letter 5
 
0.5%
Other Punctuation 2
 
0.2%
Lowercase Letter 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 44
 
5.0%
리 36
 
4.1%
라 36
 
4.1%
아 30
 
3.4%
이 26
 
3.0%
나 18
 
2.1%
속 16
 
1.8%
로 15
 
1.7%
페 14
 
1.6%
무 14
 
1.6%
Other values (236) 628
71.6%
Space Separator
ValueCountFrequency (%)
24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
x 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 877
92.3%
Common 66
 
6.9%
Latin 7
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 44
 
5.0%
리 36
 
4.1%
라 36
 
4.1%
아 30
 
3.4%
이 26
 
3.0%
나 18
 
2.1%
속 16
 
1.8%
로 15
 
1.7%
페 14
 
1.6%
무 14
 
1.6%
Other values (236) 628
71.6%
Common
ValueCountFrequency (%)
24
36.4%
( 20
30.3%
) 20
30.3%
, 2
 
3.0%
Latin
ValueCountFrequency (%)
X 5
71.4%
x 2
 
28.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 877
92.3%
ASCII 73
 
7.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 44
 
5.0%
리 36
 
4.1%
라 36
 
4.1%
아 30
 
3.4%
이 26
 
3.0%
나 18
 
2.1%
속 16
 
1.8%
로 15
 
1.7%
페 14
 
1.6%
무 14
 
1.6%
Other values (236) 628
71.6%
ASCII
ValueCountFrequency (%)
24
32.9%
( 20
27.4%
) 20
27.4%
X 5
 
6.8%
, 2
 
2.7%
x 2
 
2.7%

1998-2013
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0542169
Minimum0
Maximum453
Zeros115
Zeros (%)69.3%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T19:20:44.995587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile24.75
Maximum453
Range453
Interquartile range (IQR)1

Descriptive statistics

Standard deviation39.872411
Coefficient of variation (CV)5.6522803
Kurtosis99.67971
Mean7.0542169
Median Absolute Deviation (MAD)0
Skewness9.4804556
Sum1171
Variance1589.8092
MonotonicityNot monotonic
2023-12-12T19:20:45.092109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 115
69.3%
1 21
 
12.7%
2 5
 
3.0%
3 4
 
2.4%
5 3
 
1.8%
9 2
 
1.2%
6 2
 
1.2%
25 2
 
1.2%
22 2
 
1.2%
453 1
 
0.6%
Other values (9) 9
 
5.4%
ValueCountFrequency (%)
0 115
69.3%
1 21
 
12.7%
2 5
 
3.0%
3 4
 
2.4%
5 3
 
1.8%
6 2
 
1.2%
9 2
 
1.2%
10 1
 
0.6%
14 1
 
0.6%
22 2
 
1.2%
ValueCountFrequency (%)
453 1
0.6%
210 1
0.6%
91 1
0.6%
70 1
0.6%
44 1
0.6%
39 1
0.6%
34 1
0.6%
25 2
1.2%
24 1
0.6%
22 2
1.2%

2014
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3313253
Minimum0
Maximum16
Zeros151
Zeros (%)91.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T19:20:45.488891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.75
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6157878
Coefficient of variation (CV)4.8767415
Kurtosis61.857198
Mean0.3313253
Median Absolute Deviation (MAD)0
Skewness7.3361726
Sum55
Variance2.6107704
MonotonicityNot monotonic
2023-12-12T19:20:45.585148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 151
91.0%
1 6
 
3.6%
3 3
 
1.8%
2 2
 
1.2%
10 1
 
0.6%
16 1
 
0.6%
6 1
 
0.6%
4 1
 
0.6%
ValueCountFrequency (%)
0 151
91.0%
1 6
 
3.6%
2 2
 
1.2%
3 3
 
1.8%
4 1
 
0.6%
6 1
 
0.6%
10 1
 
0.6%
16 1
 
0.6%
ValueCountFrequency (%)
16 1
 
0.6%
10 1
 
0.6%
6 1
 
0.6%
4 1
 
0.6%
3 3
 
1.8%
2 2
 
1.2%
1 6
 
3.6%
0 151
91.0%

2015
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4939759
Minimum0
Maximum11
Zeros141
Zeros (%)84.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T19:20:45.694611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.75
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5521129
Coefficient of variation (CV)3.1420822
Kurtosis18.491287
Mean0.4939759
Median Absolute Deviation (MAD)0
Skewness4.0571667
Sum82
Variance2.4090544
MonotonicityNot monotonic
2023-12-12T19:20:45.805725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 141
84.9%
1 9
 
5.4%
6 4
 
2.4%
2 4
 
2.4%
3 3
 
1.8%
4 2
 
1.2%
11 1
 
0.6%
8 1
 
0.6%
5 1
 
0.6%
ValueCountFrequency (%)
0 141
84.9%
1 9
 
5.4%
2 4
 
2.4%
3 3
 
1.8%
4 2
 
1.2%
5 1
 
0.6%
6 4
 
2.4%
8 1
 
0.6%
11 1
 
0.6%
ValueCountFrequency (%)
11 1
 
0.6%
8 1
 
0.6%
6 4
 
2.4%
5 1
 
0.6%
4 2
 
1.2%
3 3
 
1.8%
2 4
 
2.4%
1 9
 
5.4%
0 141
84.9%

2016
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52409639
Minimum0
Maximum22
Zeros143
Zeros (%)86.1%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T19:20:45.937148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum22
Range22
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.421374
Coefficient of variation (CV)4.620093
Kurtosis52.625561
Mean0.52409639
Median Absolute Deviation (MAD)0
Skewness6.9483622
Sum87
Variance5.8630522
MonotonicityNot monotonic
2023-12-12T19:20:46.054072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 143
86.1%
1 13
 
7.8%
4 2
 
1.2%
3 2
 
1.2%
2 2
 
1.2%
17 1
 
0.6%
5 1
 
0.6%
12 1
 
0.6%
22 1
 
0.6%
ValueCountFrequency (%)
0 143
86.1%
1 13
 
7.8%
2 2
 
1.2%
3 2
 
1.2%
4 2
 
1.2%
5 1
 
0.6%
12 1
 
0.6%
17 1
 
0.6%
22 1
 
0.6%
ValueCountFrequency (%)
22 1
 
0.6%
17 1
 
0.6%
12 1
 
0.6%
5 1
 
0.6%
4 2
 
1.2%
3 2
 
1.2%
2 2
 
1.2%
1 13
 
7.8%
0 143
86.1%

2017
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42168675
Minimum0
Maximum14
Zeros147
Zeros (%)88.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T19:20:46.168234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7241281
Coefficient of variation (CV)4.0886467
Kurtosis35.494285
Mean0.42168675
Median Absolute Deviation (MAD)0
Skewness5.6603963
Sum70
Variance2.9726177
MonotonicityNot monotonic
2023-12-12T19:20:46.289197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 147
88.6%
1 8
 
4.8%
3 5
 
3.0%
11 1
 
0.6%
2 1
 
0.6%
7 1
 
0.6%
4 1
 
0.6%
14 1
 
0.6%
9 1
 
0.6%
ValueCountFrequency (%)
0 147
88.6%
1 8
 
4.8%
2 1
 
0.6%
3 5
 
3.0%
4 1
 
0.6%
7 1
 
0.6%
9 1
 
0.6%
11 1
 
0.6%
14 1
 
0.6%
ValueCountFrequency (%)
14 1
 
0.6%
11 1
 
0.6%
9 1
 
0.6%
7 1
 
0.6%
4 1
 
0.6%
3 5
 
3.0%
2 1
 
0.6%
1 8
 
4.8%
0 147
88.6%

2018
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60240964
Minimum0
Maximum17
Zeros140
Zeros (%)84.3%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T19:20:46.421723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.75
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1231181
Coefficient of variation (CV)3.5243761
Kurtosis29.425005
Mean0.60240964
Median Absolute Deviation (MAD)0
Skewness5.0559284
Sum100
Variance4.5076305
MonotonicityNot monotonic
2023-12-12T19:20:46.539272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 140
84.3%
1 11
 
6.6%
5 3
 
1.8%
2 3
 
1.8%
3 3
 
1.8%
12 1
 
0.6%
4 1
 
0.6%
9 1
 
0.6%
17 1
 
0.6%
7 1
 
0.6%
ValueCountFrequency (%)
0 140
84.3%
1 11
 
6.6%
2 3
 
1.8%
3 3
 
1.8%
4 1
 
0.6%
5 3
 
1.8%
7 1
 
0.6%
9 1
 
0.6%
10 1
 
0.6%
12 1
 
0.6%
ValueCountFrequency (%)
17 1
 
0.6%
12 1
 
0.6%
10 1
 
0.6%
9 1
 
0.6%
7 1
 
0.6%
5 3
 
1.8%
4 1
 
0.6%
3 3
 
1.8%
2 3
 
1.8%
1 11
6.6%

2019
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3313253
Minimum0
Maximum7
Zeros144
Zeros (%)86.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T19:20:46.724718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1248626
Coefficient of variation (CV)3.3950398
Kurtosis18.578239
Mean0.3313253
Median Absolute Deviation (MAD)0
Skewness4.2559241
Sum55
Variance1.2653158
MonotonicityNot monotonic
2023-12-12T19:20:46.871312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 144
86.7%
1 11
 
6.6%
2 5
 
3.0%
5 3
 
1.8%
6 2
 
1.2%
7 1
 
0.6%
ValueCountFrequency (%)
0 144
86.7%
1 11
 
6.6%
2 5
 
3.0%
5 3
 
1.8%
6 2
 
1.2%
7 1
 
0.6%
ValueCountFrequency (%)
7 1
 
0.6%
6 2
 
1.2%
5 3
 
1.8%
2 5
 
3.0%
1 11
 
6.6%
0 144
86.7%

2020
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.37349398
Minimum0
Maximum10
Zeros144
Zeros (%)86.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T19:20:47.052012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4288019
Coefficient of variation (CV)3.825502
Kurtosis32.152888
Mean0.37349398
Median Absolute Deviation (MAD)0
Skewness5.4511295
Sum62
Variance2.041475
MonotonicityNot monotonic
2023-12-12T19:20:47.183450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 144
86.7%
1 11
 
6.6%
2 4
 
2.4%
3 3
 
1.8%
10 2
 
1.2%
5 1
 
0.6%
9 1
 
0.6%
ValueCountFrequency (%)
0 144
86.7%
1 11
 
6.6%
2 4
 
2.4%
3 3
 
1.8%
5 1
 
0.6%
9 1
 
0.6%
10 2
 
1.2%
ValueCountFrequency (%)
10 2
 
1.2%
9 1
 
0.6%
5 1
 
0.6%
3 3
 
1.8%
2 4
 
2.4%
1 11
 
6.6%
0 144
86.7%

2021
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36144578
Minimum0
Maximum9
Zeros141
Zeros (%)84.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T19:20:47.300130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2218051
Coefficient of variation (CV)3.3803273
Kurtosis30.797681
Mean0.36144578
Median Absolute Deviation (MAD)0
Skewness5.1293895
Sum60
Variance1.4928076
MonotonicityNot monotonic
2023-12-12T19:20:47.407999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 141
84.9%
1 13
 
7.8%
2 4
 
2.4%
3 4
 
2.4%
9 2
 
1.2%
4 1
 
0.6%
5 1
 
0.6%
ValueCountFrequency (%)
0 141
84.9%
1 13
 
7.8%
2 4
 
2.4%
3 4
 
2.4%
4 1
 
0.6%
5 1
 
0.6%
9 2
 
1.2%
ValueCountFrequency (%)
9 2
 
1.2%
5 1
 
0.6%
4 1
 
0.6%
3 4
 
2.4%
2 4
 
2.4%
1 13
 
7.8%
0 141
84.9%

2022
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.37349398
Minimum0
Maximum12
Zeros142
Zeros (%)85.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T19:20:47.525690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.75
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3857355
Coefficient of variation (CV)3.710195
Kurtosis36.929247
Mean0.37349398
Median Absolute Deviation (MAD)0
Skewness5.6120226
Sum62
Variance1.9202629
MonotonicityNot monotonic
2023-12-12T19:20:47.672203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 142
85.5%
1 15
 
9.0%
3 3
 
1.8%
7 2
 
1.2%
2 1
 
0.6%
12 1
 
0.6%
6 1
 
0.6%
4 1
 
0.6%
ValueCountFrequency (%)
0 142
85.5%
1 15
 
9.0%
2 1
 
0.6%
3 3
 
1.8%
4 1
 
0.6%
6 1
 
0.6%
7 2
 
1.2%
12 1
 
0.6%
ValueCountFrequency (%)
12 1
 
0.6%
7 2
 
1.2%
6 1
 
0.6%
4 1
 
0.6%
3 3
 
1.8%
2 1
 
0.6%
1 15
 
9.0%
0 142
85.5%

Interactions

2023-12-12T19:20:42.567955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:32.610374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:33.723120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:34.983706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:36.160747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:37.050717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:38.079737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:39.677425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:40.761854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:41.664670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:42.657990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:32.733024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:33.859686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:35.095482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:36.245034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:37.159946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:38.203303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:39.784490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:40.841155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:41.745849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:42.756931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:32.886017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:34.005653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:35.221105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:36.350928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:37.258944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:38.710558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:39.923313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:40.934894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:41.836840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:42.858663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:32.971941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:34.120377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:35.327941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:36.439734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:37.369236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:38.826016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:40.051207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:41.020287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:41.920177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:42.940171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:33.061474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:34.212936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:35.435210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:36.530138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:37.468366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:38.928029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:40.144055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:41.103042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:42.013597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:43.047467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:33.179696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:34.344389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:35.566508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:36.610240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:37.571900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:39.051374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:40.240319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:41.196852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:42.111656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:43.140127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:33.301860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:34.465456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:35.689314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:36.704848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:37.677752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:39.197369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:40.366488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:41.298385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:42.213253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:43.242815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:33.408443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:34.603520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:35.817156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:36.802420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:37.771362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:39.343841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:40.458623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:41.391859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:42.303299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:43.351350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:33.503053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:34.733196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:35.915577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:36.876388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:37.868314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:39.462341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:40.567276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:41.469798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:42.387789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:43.457214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:33.607853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:34.851683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:36.030216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:36.953692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:37.958766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:39.559970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:40.659173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:41.564440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:42.473353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:20:47.774492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
작물분류1998-2013201420152016201720182019202020212022
작물분류1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
1998-20130.0001.0000.8300.8490.8060.8460.8370.6840.5190.0000.332
20140.0000.8301.0000.8480.9330.8070.9030.8600.4890.2050.641
20150.0000.8490.8481.0000.7160.7510.8360.7370.5620.1930.498
20160.0000.8060.9330.7161.0000.8850.8650.9090.6200.1890.485
20170.0000.8460.8070.7510.8851.0000.9380.7300.8980.7810.486
20180.0000.8370.9030.8360.8650.9381.0000.7060.7730.0000.292
20190.0000.6840.8600.7370.9090.7300.7061.0000.5820.0000.788
20200.0000.5190.4890.5620.6200.8980.7730.5821.0000.8060.407
20210.0000.0000.2050.1930.1890.7810.0000.0000.8061.0000.295
20220.0000.3320.6410.4980.4850.4860.2920.7880.4070.2951.000
2023-12-12T19:20:47.939198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1998-2013201420152016201720182019202020212022작물분류
1998-20131.0000.2800.2590.2520.4140.1160.1780.1820.0270.1360.000
20140.2801.0000.3600.2150.4250.2500.0910.202-0.0090.1760.000
20150.2590.3601.0000.4480.3630.3190.2240.1980.0230.1360.000
20160.2520.2150.4481.0000.3840.2200.2840.163-0.0610.0830.000
20170.4140.4250.3630.3841.0000.3010.2760.2770.0190.1400.000
20180.1160.2500.3190.2200.3011.0000.3070.199-0.0340.0150.000
20190.1780.0910.2240.2840.2760.3071.0000.286-0.0580.2110.000
20200.1820.2020.1980.1630.2770.1990.2861.0000.0910.1040.000
20210.027-0.0090.023-0.0610.019-0.034-0.0580.0911.0000.0600.000
20220.1360.1760.1360.0830.1400.0150.2110.1040.0601.0000.000
작물분류0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T19:20:43.613797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:20:43.799567image/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

작물분류작 물 명1998-2013201420152016201720182019202020212022
0식량작물고구마0000010000
1식량작물메밀0100000000
2식량작물벼2000001001
3식량작물옥수수0000000000
4식량작물콩1000000000
5채소류가지0000000010
6채소류고추5000000000
7채소류고추(파프리카)0000000000
8채소류딸기00601101543
9채소류상추2010010203
작물분류작 물 명1998-2013201420152016201720182019202020212022
156화훼류홍화커런트0000000000
157화훼류히페리쿰0000050000
158특용작물고추냉이0000000010
159특용작물율무1000000000
160특용작물차나무0000000000
161사료작물이탈리안라이그라스 X 페레니얼라이그라스1000000000
162버섯류느티만가닥버섯0000000110
163버섯류큰느타리버섯0000000001
164버섯류팽이버섯3000000000
165버섯류팽이버섯(팽나무버섯)0000000000