Overview

Dataset statistics

Number of variables6
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory55.4 B

Variable types

Categorical4
Numeric2

Dataset

Description제주특별자치도 산림 병해충 발생 및 방제 현황 데이터로, 2015~2019년까지 연도 및 시군구, 병해충 분류에 따른 병해충 발생 면적 및 방제 면적 정보를 제공합니다.
Author제주특별자치도
URLhttps://www.data.go.kr/data/15096942/fileData.do

Alerts

병해충대분류 has constant value ""Constant
발생면적(ha) is highly overall correlated with 방제면적(ha) and 1 other fieldsHigh correlation
방제면적(ha) is highly overall correlated with 발생면적(ha) and 1 other fieldsHigh correlation
병해충소분류 is highly overall correlated with 발생면적(ha) and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 12:08:27.756439
Analysis finished2023-12-12 12:08:28.720923
Duration0.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2015
7 
2016
7 
2017
7 
2019
5 
2018
4 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2015 7
23.3%
2016 7
23.3%
2017 7
23.3%
2019 5
16.7%
2018 4
13.3%

Length

2023-12-12T21:08:28.796841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:08:28.951526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015 7
23.3%
2016 7
23.3%
2017 7
23.3%
2019 5
16.7%
2018 4
13.3%

시군구
Categorical

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
제주시
17 
서귀포시
13 

Length

Max length4
Median length3
Mean length3.4333333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제주시
2nd row제주시
3rd row제주시
4th row제주시
5th row서귀포시

Common Values

ValueCountFrequency (%)
제주시 17
56.7%
서귀포시 13
43.3%

Length

2023-12-12T21:08:29.113912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:08:29.234335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 17
56.7%
서귀포시 13
43.3%

병해충대분류
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
일반병해충
30 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반병해충 30
100.0%

Length

2023-12-12T21:08:29.367564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:08:29.489984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반병해충 30
100.0%

병해충소분류
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
기타해충
10 
솔나방
9 
소나무재선충
6 
솔껍질깍지벌레
5 

Length

Max length7
Median length6
Mean length4.6
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row솔껍질깍지벌레
2nd row소나무재선충
3rd row솔나방
4th row기타해충
5th row소나무재선충

Common Values

ValueCountFrequency (%)
기타해충 10
33.3%
솔나방 9
30.0%
소나무재선충 6
20.0%
솔껍질깍지벌레 5
16.7%

Length

2023-12-12T21:08:29.632774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:08:29.822750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타해충 10
33.3%
솔나방 9
30.0%
소나무재선충 6
20.0%
솔껍질깍지벌레 5
16.7%

발생면적(ha)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean824.6
Minimum26
Maximum5447
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T21:08:29.970290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile30.8
Q150
median226
Q3378.25
95-th percentile5216.55
Maximum5447
Range5421
Interquartile range (IQR)328.25

Descriptive statistics

Standard deviation1578.3653
Coefficient of variation (CV)1.9140981
Kurtosis4.9091796
Mean824.6
Median Absolute Deviation (MAD)175
Skewness2.4652979
Sum24738
Variance2491237.1
MonotonicityNot monotonic
2023-12-12T21:08:30.144849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
350 3
 
10.0%
50 2
 
6.7%
45 2
 
6.7%
110 2
 
6.7%
247 1
 
3.3%
205 1
 
3.3%
47 1
 
3.3%
91 1
 
3.3%
26 1
 
3.3%
387 1
 
3.3%
Other values (15) 15
50.0%
ValueCountFrequency (%)
26 1
3.3%
29 1
3.3%
33 1
3.3%
42 1
3.3%
45 2
6.7%
47 1
3.3%
50 2
6.7%
91 1
3.3%
110 2
6.7%
150 1
3.3%
ValueCountFrequency (%)
5447 1
 
3.3%
5235 1
 
3.3%
5194 1
 
3.3%
1970 1
 
3.3%
1349 1
 
3.3%
1308 1
 
3.3%
400 1
 
3.3%
387 1
 
3.3%
352 1
 
3.3%
350 3
10.0%

방제면적(ha)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean832.7
Minimum33
Maximum5447
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T21:08:30.314789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile43.35
Q166
median249
Q3396.75
95-th percentile5216.55
Maximum5447
Range5414
Interquartile range (IQR)330.75

Descriptive statistics

Standard deviation1575.3626
Coefficient of variation (CV)1.8918729
Kurtosis4.9124152
Mean832.7
Median Absolute Deviation (MAD)172
Skewness2.4651933
Sum24981
Variance2481767.3
MonotonicityNot monotonic
2023-12-12T21:08:30.517938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
350 3
 
10.0%
50 2
 
6.7%
45 2
 
6.7%
110 2
 
6.7%
96 1
 
3.3%
205 1
 
3.3%
47 1
 
3.3%
91 1
 
3.3%
84 1
 
3.3%
387 1
 
3.3%
Other values (15) 15
50.0%
ValueCountFrequency (%)
33 1
3.3%
42 1
3.3%
45 2
6.7%
47 1
3.3%
50 2
6.7%
60 1
3.3%
84 1
3.3%
91 1
3.3%
96 1
3.3%
110 2
6.7%
ValueCountFrequency (%)
5447 1
3.3%
5235 1
3.3%
5194 1
3.3%
1970 1
3.3%
1349 1
3.3%
1308 1
3.3%
428 1
3.3%
400 1
3.3%
387 1
3.3%
352 1
3.3%

Interactions

2023-12-12T21:08:28.247379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:08:28.022533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:08:28.372875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:08:28.124342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:08:30.644868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도시군구병해충소분류발생면적(ha)방제면적(ha)
연도1.0000.0000.0000.0000.000
시군구0.0001.0000.3710.5100.510
병해충소분류0.0000.3711.0000.8530.853
발생면적(ha)0.0000.5100.8531.0001.000
방제면적(ha)0.0000.5100.8531.0001.000
2023-12-12T21:08:30.775743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구연도병해충소분류
시군구1.0000.0000.232
연도0.0001.0000.000
병해충소분류0.2320.0001.000
2023-12-12T21:08:30.898885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생면적(ha)방제면적(ha)연도시군구병해충소분류
발생면적(ha)1.0000.9410.0000.3290.506
방제면적(ha)0.9411.0000.0000.3290.506
연도0.0000.0001.0000.0000.000
시군구0.3290.3290.0001.0000.232
병해충소분류0.5060.5060.0000.2321.000

Missing values

2023-12-12T21:08:28.526790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:08:28.671948image/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

연도시군구병해충대분류병해충소분류발생면적(ha)방제면적(ha)
02015제주시일반병해충솔껍질깍지벌레24796
12015제주시일반병해충소나무재선충51945194
22015제주시일반병해충솔나방5050
32015제주시일반병해충기타해충300300
42015서귀포시일반병해충소나무재선충13491349
52015서귀포시일반병해충솔나방293293
62015서귀포시일반병해충기타해충352352
72016제주시일반병해충솔껍질깍지벌레4242
82016제주시일반병해충소나무재선충52355235
92016제주시일반병해충솔나방110110
연도시군구병해충대분류병해충소분류발생면적(ha)방제면적(ha)
202017서귀포시일반병해충기타해충350350
212018제주시일반병해충솔껍질깍지벌레2960
222018제주시일반병해충기타해충173428
232018서귀포시일반병해충솔나방150200
242018서귀포시일반병해충기타해충387387
252019제주시일반병해충솔껍질깍지벌레2684
262019제주시일반병해충솔나방9191
272019제주시일반병해충기타해충4747
282019서귀포시일반병해충솔나방4545
292019서귀포시일반병해충기타해충4545