Overview

Dataset statistics

Number of variables11
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory103.5 B

Variable types

Numeric10
Categorical1

Dataset

Description산림분야에서 발생된 안전사고 현황을 사업종별로 보여주는 자료입니다. 2010~2021년까지의 자료입니다. 산림사업 현장별 안전사고 발생형태와 요양기간을 나타내는 자료입니다.
Author산림청
URLhttps://www.data.go.kr/data/15110264/fileData.do

Alerts

벌채 is highly overall correlated with 숲가꾸기 and 8 other fieldsHigh correlation
숲가꾸기 is highly overall correlated with 벌채 and 7 other fieldsHigh correlation
병해충 is highly overall correlated with 벌채 and 8 other fieldsHigh correlation
산불 is highly overall correlated with 벌채 and 6 other fieldsHigh correlation
임도 is highly overall correlated with 벌채 and 5 other fieldsHigh correlation
사방 is highly overall correlated with 벌채 and 7 other fieldsHigh correlation
임산물채취 is highly overall correlated with 벌채 and 7 other fieldsHigh correlation
교통 is highly overall correlated with 벌채 and 5 other fieldsHigh correlation
기타 is highly overall correlated with 벌채 and 8 other fieldsHigh correlation
구분 is highly overall correlated with 벌채 and 5 other fieldsHigh correlation
병해충 has 3 (12.5%) zerosZeros
산불 has 6 (25.0%) zerosZeros
임도 has 16 (66.7%) zerosZeros
사방 has 15 (62.5%) zerosZeros
임산물채취 has 11 (45.8%) zerosZeros
교통 has 10 (41.7%) zerosZeros
기타 has 1 (4.2%) zerosZeros

Reproduction

Analysis started2023-12-11 23:08:14.415353
Analysis finished2023-12-11 23:08:23.988898
Duration9.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct12
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.5
Minimum2010
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T08:08:24.036750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010.15
Q12012.75
median2015.5
Q32018.25
95-th percentile2020.85
Maximum2021
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.5262987
Coefficient of variation (CV)0.00174959
Kurtosis-1.2156934
Mean2015.5
Median Absolute Deviation (MAD)3
Skewness0
Sum48372
Variance12.434783
MonotonicityIncreasing
2023-12-12T08:08:24.140397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2010 2
8.3%
2011 2
8.3%
2012 2
8.3%
2013 2
8.3%
2014 2
8.3%
2015 2
8.3%
2016 2
8.3%
2017 2
8.3%
2018 2
8.3%
2019 2
8.3%
Other values (2) 4
16.7%
ValueCountFrequency (%)
2010 2
8.3%
2011 2
8.3%
2012 2
8.3%
2013 2
8.3%
2014 2
8.3%
2015 2
8.3%
2016 2
8.3%
2017 2
8.3%
2018 2
8.3%
2019 2
8.3%
ValueCountFrequency (%)
2021 2
8.3%
2020 2
8.3%
2019 2
8.3%
2018 2
8.3%
2017 2
8.3%
2016 2
8.3%
2015 2
8.3%
2014 2
8.3%
2013 2
8.3%
2012 2
8.3%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
사고
12 
사망
12 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사고
2nd row사망
3rd row사고
4th row사망
5th row사고

Common Values

ValueCountFrequency (%)
사고 12
50.0%
사망 12
50.0%

Length

2023-12-12T08:08:24.258623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:08:24.357957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사고 12
50.0%
사망 12
50.0%

벌채
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean270.33333
Minimum5
Maximum683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T08:08:24.438496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5.15
Q18.75
median240
Q3515.25
95-th percentile582
Maximum683
Range678
Interquartile range (IQR)506.5

Descriptive statistics

Standard deviation271.00243
Coefficient of variation (CV)1.0024751
Kurtosis-2.0409561
Mean270.33333
Median Absolute Deviation (MAD)235
Skewness0.079339887
Sum6488
Variance73442.319
MonotonicityNot monotonic
2023-12-12T08:08:24.797400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
6 2
 
8.3%
496 2
 
8.3%
11 2
 
8.3%
9 2
 
8.3%
5 2
 
8.3%
10 2
 
8.3%
504 1
 
4.2%
7 1
 
4.2%
476 1
 
4.2%
469 1
 
4.2%
Other values (8) 8
33.3%
ValueCountFrequency (%)
5 2
8.3%
6 2
8.3%
7 1
4.2%
8 1
4.2%
9 2
8.3%
10 2
8.3%
11 2
8.3%
469 1
4.2%
476 1
4.2%
496 2
8.3%
ValueCountFrequency (%)
683 1
4.2%
585 1
4.2%
565 1
4.2%
560 1
4.2%
524 1
4.2%
519 1
4.2%
514 1
4.2%
504 1
4.2%
496 2
8.3%
476 1
4.2%

숲가꾸기
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean314
Minimum1
Maximum1259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T08:08:24.910740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median123
Q3534.75
95-th percentile1047.9
Maximum1259
Range1258
Interquartile range (IQR)530.75

Descriptive statistics

Standard deviation396.21689
Coefficient of variation (CV)1.2618372
Kurtosis0.10436565
Mean314
Median Absolute Deviation (MAD)121
Skewness1.1145182
Sum7536
Variance156987.83
MonotonicityNot monotonic
2023-12-12T08:08:25.022364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
4 3
 
12.5%
3 3
 
12.5%
6 2
 
8.3%
1259 1
 
4.2%
1 1
 
4.2%
264 1
 
4.2%
288 1
 
4.2%
230 1
 
4.2%
337 1
 
4.2%
7 1
 
4.2%
Other values (9) 9
37.5%
ValueCountFrequency (%)
1 1
 
4.2%
3 3
12.5%
4 3
12.5%
6 2
8.3%
7 1
 
4.2%
12 1
 
4.2%
16 1
 
4.2%
230 1
 
4.2%
264 1
 
4.2%
288 1
 
4.2%
ValueCountFrequency (%)
1259 1
4.2%
1065 1
4.2%
951 1
4.2%
925 1
4.2%
626 1
4.2%
585 1
4.2%
518 1
4.2%
419 1
4.2%
337 1
4.2%
288 1
4.2%

병해충
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.166667
Minimum0
Maximum246
Zeros3
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T08:08:25.121368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median49
Q3130
95-th percentile205.55
Maximum246
Range246
Interquartile range (IQR)129

Descriptive statistics

Standard deviation81.076811
Coefficient of variation (CV)1.0931705
Kurtosis-1.0470883
Mean74.166667
Median Absolute Deviation (MAD)49
Skewness0.5432112
Sum1780
Variance6573.4493
MonotonicityNot monotonic
2023-12-12T08:08:25.229903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 6
25.0%
0 3
12.5%
4 2
 
8.3%
95 1
 
4.2%
128 1
 
4.2%
94 1
 
4.2%
2 1
 
4.2%
126 1
 
4.2%
136 1
 
4.2%
150 1
 
4.2%
Other values (6) 6
25.0%
ValueCountFrequency (%)
0 3
12.5%
1 6
25.0%
2 1
 
4.2%
4 2
 
8.3%
94 1
 
4.2%
95 1
 
4.2%
109 1
 
4.2%
123 1
 
4.2%
126 1
 
4.2%
128 1
 
4.2%
ValueCountFrequency (%)
246 1
4.2%
209 1
4.2%
186 1
4.2%
162 1
4.2%
150 1
4.2%
136 1
4.2%
128 1
4.2%
126 1
4.2%
123 1
4.2%
109 1
4.2%

산불
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.083333
Minimum0
Maximum121
Zeros6
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T08:08:25.327819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median2.5
Q362.75
95-th percentile101.5
Maximum121
Range121
Interquartile range (IQR)62

Descriptive statistics

Standard deviation40.553686
Coefficient of variation (CV)1.5547739
Kurtosis-0.058720667
Mean26.083333
Median Absolute Deviation (MAD)2.5
Skewness1.2416122
Sum626
Variance1644.6014
MonotonicityNot monotonic
2023-12-12T08:08:25.443744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 6
25.0%
1 5
20.8%
3 2
 
8.3%
5 2
 
8.3%
68 1
 
4.2%
103 1
 
4.2%
2 1
 
4.2%
61 1
 
4.2%
121 1
 
4.2%
93 1
 
4.2%
Other values (3) 3
12.5%
ValueCountFrequency (%)
0 6
25.0%
1 5
20.8%
2 1
 
4.2%
3 2
 
8.3%
4 1
 
4.2%
5 2
 
8.3%
61 1
 
4.2%
68 1
 
4.2%
69 1
 
4.2%
84 1
 
4.2%
ValueCountFrequency (%)
121 1
4.2%
103 1
4.2%
93 1
4.2%
84 1
4.2%
69 1
4.2%
68 1
4.2%
61 1
4.2%
5 2
8.3%
4 1
4.2%
3 2
8.3%

임도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1666667
Minimum0
Maximum8
Zeros16
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T08:08:25.552372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6.55
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2198054
Coefficient of variation (CV)1.9026904
Kurtosis4.5514481
Mean1.1666667
Median Absolute Deviation (MAD)0
Skewness2.2428135
Sum28
Variance4.9275362
MonotonicityNot monotonic
2023-12-12T08:08:25.655850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 16
66.7%
2 4
 
16.7%
7 1
 
4.2%
4 1
 
4.2%
8 1
 
4.2%
1 1
 
4.2%
ValueCountFrequency (%)
0 16
66.7%
1 1
 
4.2%
2 4
 
16.7%
4 1
 
4.2%
7 1
 
4.2%
8 1
 
4.2%
ValueCountFrequency (%)
8 1
 
4.2%
7 1
 
4.2%
4 1
 
4.2%
2 4
 
16.7%
1 1
 
4.2%
0 16
66.7%

사방
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0416667
Minimum0
Maximum6
Zeros15
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T08:08:25.764204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4.7
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7062332
Coefficient of variation (CV)1.6379839
Kurtosis2.5579311
Mean1.0416667
Median Absolute Deviation (MAD)0
Skewness1.7629671
Sum25
Variance2.9112319
MonotonicityNot monotonic
2023-12-12T08:08:25.860489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 15
62.5%
2 3
 
12.5%
3 2
 
8.3%
1 2
 
8.3%
6 1
 
4.2%
5 1
 
4.2%
ValueCountFrequency (%)
0 15
62.5%
1 2
 
8.3%
2 3
 
12.5%
3 2
 
8.3%
5 1
 
4.2%
6 1
 
4.2%
ValueCountFrequency (%)
6 1
 
4.2%
5 1
 
4.2%
3 2
 
8.3%
2 3
 
12.5%
1 2
 
8.3%
0 15
62.5%

임산물채취
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5416667
Minimum0
Maximum35
Zeros11
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T08:08:25.969408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33.75
95-th percentile17.85
Maximum35
Range35
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation8.4027903
Coefficient of variation (CV)1.8501557
Kurtosis7.0469314
Mean4.5416667
Median Absolute Deviation (MAD)1
Skewness2.545942
Sum109
Variance70.606884
MonotonicityNot monotonic
2023-12-12T08:08:26.086136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 11
45.8%
1 3
 
12.5%
2 3
 
12.5%
35 1
 
4.2%
11 1
 
4.2%
10 1
 
4.2%
6 1
 
4.2%
18 1
 
4.2%
17 1
 
4.2%
3 1
 
4.2%
ValueCountFrequency (%)
0 11
45.8%
1 3
 
12.5%
2 3
 
12.5%
3 1
 
4.2%
6 1
 
4.2%
10 1
 
4.2%
11 1
 
4.2%
17 1
 
4.2%
18 1
 
4.2%
35 1
 
4.2%
ValueCountFrequency (%)
35 1
 
4.2%
18 1
 
4.2%
17 1
 
4.2%
11 1
 
4.2%
10 1
 
4.2%
6 1
 
4.2%
3 1
 
4.2%
2 3
 
12.5%
1 3
 
12.5%
0 11
45.8%

교통
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9583333
Minimum0
Maximum36
Zeros10
Zeros (%)41.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T08:08:26.213984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35.25
95-th percentile30.7
Maximum36
Range36
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation10.584974
Coefficient of variation (CV)1.7764991
Kurtosis3.3434823
Mean5.9583333
Median Absolute Deviation (MAD)1
Skewness2.1110713
Sum143
Variance112.04167
MonotonicityNot monotonic
2023-12-12T08:08:26.322373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 10
41.7%
1 3
 
12.5%
5 2
 
8.3%
2 2
 
8.3%
36 1
 
4.2%
31 1
 
4.2%
7 1
 
4.2%
29 1
 
4.2%
4 1
 
4.2%
13 1
 
4.2%
ValueCountFrequency (%)
0 10
41.7%
1 3
 
12.5%
2 2
 
8.3%
4 1
 
4.2%
5 2
 
8.3%
6 1
 
4.2%
7 1
 
4.2%
13 1
 
4.2%
29 1
 
4.2%
31 1
 
4.2%
ValueCountFrequency (%)
36 1
 
4.2%
31 1
 
4.2%
29 1
 
4.2%
13 1
 
4.2%
7 1
 
4.2%
6 1
 
4.2%
5 2
8.3%
4 1
 
4.2%
2 2
8.3%
1 3
12.5%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.25
Minimum0
Maximum190
Zeros1
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T08:08:26.448201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.75
median14.5
Q3100.75
95-th percentile166.85
Maximum190
Range190
Interquartile range (IQR)99

Descriptive statistics

Standard deviation66.343802
Coefficient of variation (CV)1.1794454
Kurtosis-0.89654152
Mean56.25
Median Absolute Deviation (MAD)14
Skewness0.79962456
Sum1350
Variance4401.5
MonotonicityNot monotonic
2023-12-12T08:08:26.571237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 5
20.8%
3 3
12.5%
73 2
 
8.3%
23 1
 
4.2%
2 1
 
4.2%
94 1
 
4.2%
139 1
 
4.2%
79 1
 
4.2%
0 1
 
4.2%
47 1
 
4.2%
Other values (7) 7
29.2%
ValueCountFrequency (%)
0 1
 
4.2%
1 5
20.8%
2 1
 
4.2%
3 3
12.5%
4 1
 
4.2%
6 1
 
4.2%
23 1
 
4.2%
47 1
 
4.2%
73 2
 
8.3%
79 1
 
4.2%
ValueCountFrequency (%)
190 1
4.2%
167 1
4.2%
166 1
4.2%
152 1
4.2%
139 1
4.2%
121 1
4.2%
94 1
4.2%
79 1
4.2%
73 2
8.3%
47 1
4.2%

Interactions

2023-12-12T08:08:22.843560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:14.696878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:15.404342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:16.205716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:17.016008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:17.881940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:18.790268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:19.996108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:20.879095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:21.805392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:22.938969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:14.759190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:15.493269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:16.273455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:17.120759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:17.970535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:18.900339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:20.080957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:20.949031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:21.934019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:23.025141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:14.831754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:15.568059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:16.350759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:17.216944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:18.053948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:18.991489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:20.178168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:21.030027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:22.037368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:23.144481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:14.903304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:15.638547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:16.423893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:17.298919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:18.146932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:19.085829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:20.261436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:21.113869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:22.142148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:23.225726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:14.971184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:15.708939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:16.500549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:17.366663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:18.234782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:19.426822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:20.357203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:21.211679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:22.233194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:23.309585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:15.039846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:15.795233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:16.577939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:17.453507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:18.313598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:19.524785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:20.439234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:21.299415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:22.318101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:23.399326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:15.115903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:15.887519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:16.658381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:17.553671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:18.399975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:19.624445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:20.524584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:21.396721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:22.408737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:23.485946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:15.185905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:15.968379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:16.732967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:17.640862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:18.478517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:19.705580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:20.604512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:21.479502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:22.510688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:23.570502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:15.258367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:16.054709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:16.812184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:17.722723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:18.573364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:19.799822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:20.701417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:21.586491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:22.615962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:23.670039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:15.329338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:16.131439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:16.908724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:17.803643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:18.700841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:19.891343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:20.804050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:21.688454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:08:22.721171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:08:26.661079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분벌채숲가꾸기병해충산불임도사방임산물채취교통기타
연도1.0000.0000.0000.2210.0000.5130.4240.3490.5420.0000.000
구분0.0001.0001.0001.0001.0000.6900.8030.8880.3110.4131.000
벌채0.0001.0001.0000.7830.8380.5760.5330.7410.2560.7400.790
숲가꾸기0.2211.0000.7831.0000.9310.8000.8030.8180.8920.8570.928
병해충0.0001.0000.8380.9311.0000.9340.9420.8330.8760.6600.877
산불0.5130.6900.5760.8000.9341.0000.9640.9050.9010.6130.875
임도0.4240.8030.5330.8030.9420.9641.0000.9760.8610.0000.949
사방0.3490.8880.7410.8180.8330.9050.9761.0000.7230.3030.942
임산물채취0.5420.3110.2560.8920.8760.9010.8610.7231.0000.8620.903
교통0.0000.4130.7400.8570.6600.6130.0000.3030.8621.0000.831
기타0.0001.0000.7900.9280.8770.8750.9490.9420.9030.8311.000
2023-12-12T08:08:26.793127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도벌채숲가꾸기병해충산불임도사방임산물채취교통기타구분
연도1.000-0.120-0.4060.085-0.4280.2260.215-0.182-0.304-0.1730.000
벌채-0.1201.0000.7600.8240.7270.5070.5670.6130.6040.7420.929
숲가꾸기-0.4060.7601.0000.7370.8140.4870.5480.6790.7410.8700.853
병해충0.0850.8240.7371.0000.6500.6340.7050.5370.5820.7390.853
산불-0.4280.7270.8140.6501.0000.4840.5310.6280.5600.7810.451
임도0.2260.5070.4870.6340.4841.0000.9240.5860.1660.5630.544
사방0.2150.5670.5480.7050.5310.9241.0000.5260.2310.5920.632
임산물채취-0.1820.6130.6790.5370.6280.5860.5261.0000.5980.5520.275
교통-0.3040.6040.7410.5820.5600.1660.2310.5981.0000.5620.462
기타-0.1730.7420.8700.7390.7810.5630.5920.5520.5621.0000.826
구분0.0000.9290.8530.8530.4510.5440.6320.2750.4620.8261.000

Missing values

2023-12-12T08:08:23.799432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:08:23.939811image/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

연도구분벌채숲가꾸기병해충산불임도사방임산물채취교통기타
02010사고50412599568003536167
12010사망5161100054
22011사고496106510910373110190
32011사망1140200101
42012사고51995112361001031121
52012사망1161100011
62013사고5859251621210105166
72013사망860300003
82014사고560626246934267152
92014사망9124100026
연도구분벌채숲가꾸기병해충산불임도사방임산물채취교통기타
142017사고514419128523171323
152017사망670000111
162018사고4963371501152247
172018사망544000000
182019사고5652301361212179
192019사망1031000003
202020사고46928812642200139
212020사망932000003
222021사고476264945223694
232021사망731000002