Overview

Dataset statistics

Number of variables11
Number of observations23
Missing cells18
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory103.7 B

Variable types

Text1
Numeric10

Dataset

Description민유림(전국 17개시도)과 국유림(동부, 서부, 남부, 중부, 북부국유림관리소, 기타 관할 구역) 2011년도부터 2020년까지(10년간) 임도를 신설한 실적
Author산림청
URLhttps://www.data.go.kr/data/15094372/fileData.do

Alerts

2011년 is highly overall correlated with 2012년 and 8 other fieldsHigh correlation
2012년 is highly overall correlated with 2011년 and 8 other fieldsHigh correlation
2013년 is highly overall correlated with 2011년 and 8 other fieldsHigh correlation
2014년 is highly overall correlated with 2011년 and 8 other fieldsHigh correlation
2015년 is highly overall correlated with 2011년 and 8 other fieldsHigh correlation
2016년 is highly overall correlated with 2011년 and 8 other fieldsHigh correlation
2017년 is highly overall correlated with 2011년 and 8 other fieldsHigh correlation
2018년 is highly overall correlated with 2011년 and 8 other fieldsHigh correlation
2019년 is highly overall correlated with 2011년 and 8 other fieldsHigh correlation
2020년 is highly overall correlated with 2011년 and 8 other fieldsHigh correlation
2011년 has 1 (4.3%) missing valuesMissing
2012년 has 1 (4.3%) missing valuesMissing
2013년 has 1 (4.3%) missing valuesMissing
2014년 has 3 (13.0%) missing valuesMissing
2015년 has 3 (13.0%) missing valuesMissing
2016년 has 2 (8.7%) missing valuesMissing
2017년 has 2 (8.7%) missing valuesMissing
2018년 has 1 (4.3%) missing valuesMissing
2019년 has 2 (8.7%) missing valuesMissing
2020년 has 2 (8.7%) missing valuesMissing
구분(km) has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:02:14.431029
Analysis finished2023-12-12 09:02:27.241623
Duration12.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분(km)
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-12T18:02:27.423042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length3.2173913
Min length2

Characters and Unicode

Total characters74
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row서울
2nd row부산
3rd row대구
4th row인천
5th row광주
ValueCountFrequency (%)
서울 1
 
4.3%
전북 1
 
4.3%
서부지방산림청 1
 
4.3%
중부지방산림청 1
 
4.3%
남부지방산림청 1
 
4.3%
동부지방산림청 1
 
4.3%
북부지방산림청 1
 
4.3%
제주 1
 
4.3%
경남 1
 
4.3%
경북 1
 
4.3%
Other values (13) 13
56.5%
2023-12-12T18:02:27.747692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 7
 
9.5%
부 6
 
8.1%
림 6
 
8.1%
지 5
 
6.8%
방 5
 
6.8%
청 5
 
6.8%
남 4
 
5.4%
북 4
 
5.4%
경 3
 
4.1%
전 3
 
4.1%
Other values (20) 26
35.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 74
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 7
 
9.5%
부 6
 
8.1%
림 6
 
8.1%
지 5
 
6.8%
방 5
 
6.8%
청 5
 
6.8%
남 4
 
5.4%
북 4
 
5.4%
경 3
 
4.1%
전 3
 
4.1%
Other values (20) 26
35.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 74
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 7
 
9.5%
부 6
 
8.1%
림 6
 
8.1%
지 5
 
6.8%
방 5
 
6.8%
청 5
 
6.8%
남 4
 
5.4%
북 4
 
5.4%
경 3
 
4.1%
전 3
 
4.1%
Other values (20) 26
35.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 74
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 7
 
9.5%
부 6
 
8.1%
림 6
 
8.1%
지 5
 
6.8%
방 5
 
6.8%
청 5
 
6.8%
남 4
 
5.4%
북 4
 
5.4%
경 3
 
4.1%
전 3
 
4.1%
Other values (20) 26
35.1%

2011년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)72.7%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean26
Minimum1
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T18:02:27.892529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.05
Q13
median23.5
Q344
95-th percentile60.75
Maximum73
Range72
Interquartile range (IQR)41

Descriptive statistics

Standard deviation23.18456
Coefficient of variation (CV)0.89171384
Kurtosis-1.1000655
Mean26
Median Absolute Deviation (MAD)20.5
Skewness0.46892232
Sum572
Variance537.52381
MonotonicityNot monotonic
2023-12-12T18:02:28.011312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 3
13.0%
27 2
 
8.7%
2 2
 
8.7%
52 2
 
8.7%
1 2
 
8.7%
35 1
 
4.3%
15 1
 
4.3%
20 1
 
4.3%
56 1
 
4.3%
37 1
 
4.3%
Other values (6) 6
26.1%
ValueCountFrequency (%)
1 2
8.7%
2 2
8.7%
3 3
13.0%
5 1
 
4.3%
11 1
 
4.3%
15 1
 
4.3%
20 1
 
4.3%
27 2
8.7%
35 1
 
4.3%
37 1
 
4.3%
ValueCountFrequency (%)
73 1
4.3%
61 1
4.3%
56 1
4.3%
52 2
8.7%
45 1
4.3%
41 1
4.3%
37 1
4.3%
35 1
4.3%
27 2
8.7%
20 1
4.3%

2012년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)86.4%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean26
Minimum1
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T18:02:28.143463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.05
Q13.5
median24.5
Q339.5
95-th percentile65.65
Maximum77
Range76
Interquartile range (IQR)36

Descriptive statistics

Standard deviation22.686266
Coefficient of variation (CV)0.87254869
Kurtosis-0.31591684
Mean26
Median Absolute Deviation (MAD)19
Skewness0.68647836
Sum572
Variance514.66667
MonotonicityNot monotonic
2023-12-12T18:02:28.326916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 2
 
8.7%
2 2
 
8.7%
1 2
 
8.7%
38 1
 
4.3%
18 1
 
4.3%
26 1
 
4.3%
22 1
 
4.3%
27 1
 
4.3%
43 1
 
4.3%
77 1
 
4.3%
Other values (9) 9
39.1%
ValueCountFrequency (%)
1 2
8.7%
2 2
8.7%
3 2
8.7%
5 1
4.3%
7 1
4.3%
18 1
4.3%
22 1
4.3%
23 1
4.3%
26 1
4.3%
27 1
4.3%
ValueCountFrequency (%)
77 1
4.3%
66 1
4.3%
59 1
4.3%
48 1
4.3%
43 1
4.3%
40 1
4.3%
38 1
4.3%
33 1
4.3%
28 1
4.3%
27 1
4.3%

2013년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)77.3%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean30.318182
Minimum2
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T18:02:28.444512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.05
Q14.25
median34
Q348.5
95-th percentile69.35
Maximum71
Range69
Interquartile range (IQR)44.25

Descriptive statistics

Standard deviation23.495463
Coefficient of variation (CV)0.77496281
Kurtosis-1.3026178
Mean30.318182
Median Absolute Deviation (MAD)20.5
Skewness0.11145672
Sum667
Variance552.0368
MonotonicityNot monotonic
2023-12-12T18:02:28.596947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3 3
13.0%
2 2
 
8.7%
25 2
 
8.7%
49 2
 
8.7%
70 1
 
4.3%
47 1
 
4.3%
35 1
 
4.3%
42 1
 
4.3%
52 1
 
4.3%
5 1
 
4.3%
Other values (7) 7
30.4%
ValueCountFrequency (%)
2 2
8.7%
3 3
13.0%
4 1
 
4.3%
5 1
 
4.3%
7 1
 
4.3%
25 2
8.7%
33 1
 
4.3%
35 1
 
4.3%
39 1
 
4.3%
42 1
 
4.3%
ValueCountFrequency (%)
71 1
4.3%
70 1
4.3%
57 1
4.3%
52 1
4.3%
49 2
8.7%
47 1
4.3%
44 1
4.3%
42 1
4.3%
39 1
4.3%
35 1
4.3%

2014년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)90.0%
Missing3
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean34.65
Minimum1
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T18:02:28.711606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.95
Q15.75
median32.5
Q350.5
95-th percentile83.25
Maximum88
Range87
Interquartile range (IQR)44.75

Descriptive statistics

Standard deviation27.3732
Coefficient of variation (CV)0.78999136
Kurtosis-0.78795076
Mean34.65
Median Absolute Deviation (MAD)23
Skewness0.37703149
Sum693
Variance749.29211
MonotonicityNot monotonic
2023-12-12T18:02:28.810194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
46 2
 
8.7%
2 2
 
8.7%
6 1
 
4.3%
16 1
 
4.3%
28 1
 
4.3%
27 1
 
4.3%
45 1
 
4.3%
50 1
 
4.3%
65 1
 
4.3%
1 1
 
4.3%
Other values (8) 8
34.8%
(Missing) 3
 
13.0%
ValueCountFrequency (%)
1 1
4.3%
2 2
8.7%
3 1
4.3%
5 1
4.3%
6 1
4.3%
16 1
4.3%
27 1
4.3%
28 1
4.3%
32 1
4.3%
33 1
4.3%
ValueCountFrequency (%)
88 1
4.3%
83 1
4.3%
65 1
4.3%
63 1
4.3%
52 1
4.3%
50 1
4.3%
46 2
8.7%
45 1
4.3%
33 1
4.3%
32 1
4.3%

2015년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)80.0%
Missing3
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean32.5
Minimum1
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T18:02:28.912082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.95
Q13.75
median34.5
Q345
95-th percentile84.05
Maximum85
Range84
Interquartile range (IQR)41.25

Descriptive statistics

Standard deviation26.015178
Coefficient of variation (CV)0.80046701
Kurtosis-0.35523172
Mean32.5
Median Absolute Deviation (MAD)17.5
Skewness0.47725783
Sum650
Variance676.78947
MonotonicityNot monotonic
2023-12-12T18:02:29.026278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
42 2
 
8.7%
44 2
 
8.7%
2 2
 
8.7%
3 2
 
8.7%
57 1
 
4.3%
28 1
 
4.3%
40 1
 
4.3%
84 1
 
4.3%
1 1
 
4.3%
4 1
 
4.3%
Other values (6) 6
26.1%
(Missing) 3
13.0%
ValueCountFrequency (%)
1 1
4.3%
2 2
8.7%
3 2
8.7%
4 1
4.3%
16 1
4.3%
25 1
4.3%
28 1
4.3%
29 1
4.3%
40 1
4.3%
42 2
8.7%
ValueCountFrequency (%)
85 1
4.3%
84 1
4.3%
57 1
4.3%
51 1
4.3%
48 1
4.3%
44 2
8.7%
42 2
8.7%
40 1
4.3%
29 1
4.3%
28 1
4.3%

2016년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)85.7%
Missing2
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean29.47619
Minimum1
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T18:02:29.134402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median30
Q345
95-th percentile69
Maximum76
Range75
Interquartile range (IQR)42

Descriptive statistics

Standard deviation23.919906
Coefficient of variation (CV)0.81149924
Kurtosis-1.0531588
Mean29.47619
Median Absolute Deviation (MAD)22
Skewness0.27436933
Sum619
Variance572.1619
MonotonicityNot monotonic
2023-12-12T18:02:29.261646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
3 3
 
13.0%
2 2
 
8.7%
23 1
 
4.3%
52 1
 
4.3%
36 1
 
4.3%
41 1
 
4.3%
45 1
 
4.3%
76 1
 
4.3%
1 1
 
4.3%
49 1
 
4.3%
Other values (8) 8
34.8%
(Missing) 2
 
8.7%
ValueCountFrequency (%)
1 1
 
4.3%
2 2
8.7%
3 3
13.0%
5 1
 
4.3%
11 1
 
4.3%
23 1
 
4.3%
29 1
 
4.3%
30 1
 
4.3%
36 1
 
4.3%
39 1
 
4.3%
ValueCountFrequency (%)
76 1
4.3%
69 1
4.3%
56 1
4.3%
52 1
4.3%
49 1
4.3%
45 1
4.3%
44 1
4.3%
41 1
4.3%
39 1
4.3%
36 1
4.3%

2017년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)85.7%
Missing2
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean33.666667
Minimum1
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T18:02:29.380275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median43
Q350
95-th percentile71
Maximum79
Range78
Interquartile range (IQR)46

Descriptive statistics

Standard deviation26.161677
Coefficient of variation (CV)0.77707951
Kurtosis-1.4052199
Mean33.666667
Median Absolute Deviation (MAD)24
Skewness0.044950331
Sum707
Variance684.43333
MonotonicityNot monotonic
2023-12-12T18:02:29.493642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
3 3
 
13.0%
45 2
 
8.7%
67 1
 
4.3%
17 1
 
4.3%
56 1
 
4.3%
59 1
 
4.3%
71 1
 
4.3%
5 1
 
4.3%
43 1
 
4.3%
2 1
 
4.3%
Other values (8) 8
34.8%
(Missing) 2
 
8.7%
ValueCountFrequency (%)
1 1
 
4.3%
2 1
 
4.3%
3 3
13.0%
4 1
 
4.3%
5 1
 
4.3%
17 1
 
4.3%
23 1
 
4.3%
36 1
 
4.3%
43 1
 
4.3%
45 2
8.7%
ValueCountFrequency (%)
79 1
4.3%
71 1
4.3%
67 1
4.3%
59 1
4.3%
56 1
4.3%
50 1
4.3%
49 1
4.3%
46 1
4.3%
45 2
8.7%
43 1
4.3%

2018년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)81.8%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean32.045455
Minimum1
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T18:02:29.606665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.05
Q13.5
median38
Q351.75
95-th percentile69.85
Maximum82
Range81
Interquartile range (IQR)48.25

Descriptive statistics

Standard deviation26.677041
Coefficient of variation (CV)0.83247503
Kurtosis-1.3501232
Mean32.045455
Median Absolute Deviation (MAD)26
Skewness0.16816733
Sum705
Variance711.6645
MonotonicityNot monotonic
2023-12-12T18:02:29.779306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 3
 
13.0%
1 2
 
8.7%
5 2
 
8.7%
53 1
 
4.3%
24 1
 
4.3%
44 1
 
4.3%
47 1
 
4.3%
58 1
 
4.3%
54 1
 
4.3%
67 1
 
4.3%
Other values (8) 8
34.8%
ValueCountFrequency (%)
1 2
8.7%
2 3
13.0%
3 1
 
4.3%
5 2
8.7%
15 1
 
4.3%
24 1
 
4.3%
36 1
 
4.3%
40 1
 
4.3%
44 1
 
4.3%
46 1
 
4.3%
ValueCountFrequency (%)
82 1
4.3%
70 1
4.3%
67 1
4.3%
58 1
4.3%
54 1
4.3%
53 1
4.3%
48 1
4.3%
47 1
4.3%
46 1
4.3%
44 1
4.3%

2019년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)81.0%
Missing2
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean33.095238
Minimum1
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T18:02:29.917828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median38
Q352
95-th percentile71
Maximum75
Range74
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.585531
Coefficient of variation (CV)0.80330382
Kurtosis-1.5375976
Mean33.095238
Median Absolute Deviation (MAD)29
Skewness0.12167394
Sum695
Variance706.79048
MonotonicityNot monotonic
2023-12-12T18:02:30.055955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3 2
 
8.7%
40 2
 
8.7%
1 2
 
8.7%
4 2
 
8.7%
55 1
 
4.3%
35 1
 
4.3%
50 1
 
4.3%
14 1
 
4.3%
71 1
 
4.3%
51 1
 
4.3%
Other values (7) 7
30.4%
(Missing) 2
 
8.7%
ValueCountFrequency (%)
1 2
8.7%
3 2
8.7%
4 2
8.7%
7 1
4.3%
14 1
4.3%
15 1
4.3%
35 1
4.3%
38 1
4.3%
40 2
8.7%
50 1
4.3%
ValueCountFrequency (%)
75 1
4.3%
71 1
4.3%
69 1
4.3%
67 1
4.3%
55 1
4.3%
52 1
4.3%
51 1
4.3%
50 1
4.3%
40 2
8.7%
38 1
4.3%

2020년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)100.0%
Missing2
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean35.466667
Minimum0.7
Maximum113.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T18:02:30.193514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.8
Q14.8
median34.9
Q351.7
95-th percentile78.2
Maximum113.5
Range112.8
Interquartile range (IQR)46.9

Descriptive statistics

Standard deviation31.085951
Coefficient of variation (CV)0.87648357
Kurtosis0.21193359
Mean35.466667
Median Absolute Deviation (MAD)25.8
Skewness0.75225417
Sum744.8
Variance966.33633
MonotonicityNot monotonic
2023-12-12T18:02:30.311333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1.8 1
 
4.3%
17.3 1
 
4.3%
34.9 1
 
4.3%
45.1 1
 
4.3%
59.3 1
 
4.3%
74.9 1
 
4.3%
78.2 1
 
4.3%
3.2 1
 
4.3%
60.7 1
 
4.3%
51.7 1
 
4.3%
Other values (11) 11
47.8%
(Missing) 2
 
8.7%
ValueCountFrequency (%)
0.7 1
4.3%
1.8 1
4.3%
3.2 1
4.3%
3.5 1
4.3%
3.9 1
4.3%
4.8 1
4.3%
5.7 1
4.3%
17.3 1
4.3%
17.8 1
4.3%
28.6 1
4.3%
ValueCountFrequency (%)
113.5 1
4.3%
78.2 1
4.3%
74.9 1
4.3%
60.7 1
4.3%
59.3 1
4.3%
51.7 1
4.3%
49.7 1
4.3%
45.9 1
4.3%
45.1 1
4.3%
43.6 1
4.3%

Interactions

2023-12-12T18:02:25.813406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:14.839231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:16.046311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:17.103526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:18.578511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:19.630045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:20.752262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:21.912591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:23.132447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:24.693700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:25.912942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:14.994349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:16.143856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:17.215929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:18.693063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:19.740413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:20.876725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:22.057946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:23.268056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:24.827219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:26.039069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:15.153378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:16.256717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:17.647307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:18.805777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:19.844871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:21.006572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:22.181492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:23.378718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:24.939268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:26.143723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:15.266869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:16.347911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:17.772045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:18.915428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:19.950624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:21.118675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:22.290464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:23.512802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:25.052927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:26.247383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:15.363426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:16.454124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:17.873211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:19.016940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:20.051273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:21.218003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:22.400572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:23.632003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:25.161797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:26.335865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:15.474357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:16.561268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:17.973677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:19.112889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:20.207633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:21.304036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:22.524270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:23.748185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:25.290098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:26.433447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:15.598132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:16.667779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:18.079699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:19.202956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:20.301462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:21.397931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:22.641643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:23.863665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:25.388511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:26.528879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:15.727889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:16.763722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:18.211190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:19.315203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:20.398260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:21.518760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:22.752413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:23.999932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:25.486630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:26.617213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:15.857904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:16.880193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:18.355915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:19.428784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:20.503120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:21.646730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:22.880301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:24.140522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:25.583099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:26.701178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:15.957867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:17.005384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:18.473174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:19.540438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:20.640006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:21.777197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:23.016646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:24.575773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:25.691647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:02:30.733373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분(km)2011년2012년2013년2014년2015년2016년2017년2018년2019년2020년
구분(km)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2011년1.0001.0000.8760.8770.8870.8810.9030.8640.8580.9190.874
2012년1.0000.8761.0000.8680.8230.7870.9360.8900.9190.9050.848
2013년1.0000.8770.8681.0000.9120.8280.8600.7390.7860.8680.735
2014년1.0000.8870.8230.9121.0000.9530.9040.7620.8060.8600.725
2015년1.0000.8810.7870.8280.9531.0000.9010.8940.8510.9460.929
2016년1.0000.9030.9360.8600.9040.9011.0000.8880.9570.8930.890
2017년1.0000.8640.8900.7390.7620.8940.8881.0000.9200.9460.971
2018년1.0000.8580.9190.7860.8060.8510.9570.9201.0000.7550.897
2019년1.0000.9190.9050.8680.8600.9460.8930.9460.7551.0000.947
2020년1.0000.8740.8480.7350.7250.9290.8900.9710.8970.9471.000
2023-12-12T18:02:30.863194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2011년2012년2013년2014년2015년2016년2017년2018년2019년2020년
2011년1.0000.8570.8390.8260.8430.8430.9100.9200.8940.901
2012년0.8571.0000.9370.9650.9440.9630.9080.8710.9320.876
2013년0.8390.9371.0000.9240.9160.9200.8720.8830.9270.834
2014년0.8260.9650.9241.0000.9700.9600.9050.9010.9400.850
2015년0.8430.9440.9160.9701.0000.9650.9230.9260.9610.852
2016년0.8430.9630.9200.9600.9651.0000.9170.9120.9550.890
2017년0.9100.9080.8720.9050.9230.9171.0000.9660.9550.895
2018년0.9200.8710.8830.9010.9260.9120.9661.0000.9560.918
2019년0.8940.9320.9270.9400.9610.9550.9550.9561.0000.907
2020년0.9010.8760.8340.8500.8520.8900.8950.9180.9071.000

Missing values

2023-12-12T18:02:26.819345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:02:26.973078image/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.
2023-12-12T18:02:27.127823image/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

구분(km)2011년2012년2013년2014년2015년2016년2017년2018년2019년2020년
0서울<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1부산3331232235.7
2대구313<NA><NA>24511.8
3인천3342221113.9
4광주222<NA><NA><NA><NA>1<NA><NA>
5대전2233113234.8
6울산1775333240.7
7세종1122333343.5
8경기15232532282323151417.8
9강원35595763575249465543.6
구분(km)2011년2012년2013년2014년2015년2016년2017년2018년2019년2020년
13전남566671838476798271113.5
14경북52487088856967707551.7
15경남37384952444943535160.7
16제주5556455573.2
17북부지방산림청61774965515671676778.2
18동부지방산림청73435250444459546974.9
19남부지방산림청45274245483956585259.3
20중부지방산림청41222527252945474045.1
21서부지방산림청52263528293045443834.9
22기타국유림11184716161117241517.3