Overview

Dataset statistics

Number of variables10
Number of observations22
Missing cells1
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory93.0 B

Variable types

Categorical2
Text2
Numeric6

Dataset

Description산림청소관국유림, 공사유림 및 타부처국유림 산림보호구역( 재해방지보호구역, 생활환경보호구역, 경관보호구역, 1종수원함양보호구역, 2종수원함양보호구역, 3종수원함양보호구역, 산림유전자원 보호구역) 지정현황
Author산림청
URLhttps://www.data.go.kr/data/15090600/fileData.do

Alerts

재해방지보호구역(ha) is highly overall correlated with 경관보호구역(ha) and 3 other fieldsHigh correlation
경관보호구역(ha) is highly overall correlated with 재해방지보호구역(ha) and 4 other fieldsHigh correlation
1종수원함양보호구역(ha) is highly overall correlated with 재해방지보호구역(ha) and 2 other fieldsHigh correlation
2종수원함양보호구역(ha) is highly overall correlated with 경관보호구역(ha) and 2 other fieldsHigh correlation
3종수원함양보호구역(ha) is highly overall correlated with 산림유전자원 보호구역(ha) and 1 other fieldsHigh correlation
산림유전자원 보호구역(ha) is highly overall correlated with 경관보호구역(ha) and 4 other fieldsHigh correlation
분류 is highly overall correlated with 재해방지보호구역(ha) and 2 other fieldsHigh correlation
생활환경보호구역(ha) is highly overall correlated with 재해방지보호구역(ha) and 4 other fieldsHigh correlation
생활환경보호구역(ha) is highly imbalanced (66.5%)Imbalance
1종수원함양보호구역(ha) has 1 (4.5%) missing valuesMissing
지역 has unique valuesUnique
지역영문명 has unique valuesUnique
재해방지보호구역(ha) has 11 (50.0%) zerosZeros
경관보호구역(ha) has 8 (36.4%) zerosZeros
1종수원함양보호구역(ha) has 6 (27.3%) zerosZeros
2종수원함양보호구역(ha) has 14 (63.6%) zerosZeros
3종수원함양보호구역(ha) has 11 (50.0%) zerosZeros
산림유전자원 보호구역(ha) has 7 (31.8%) zerosZeros

Reproduction

Analysis started2023-12-12 14:49:27.215996
Analysis finished2023-12-12 14:49:34.409711
Duration7.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
공사유림,타부처국유림
17 
산림청소관국유림
5 

Length

Max length11
Median length11
Mean length10.318182
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공사유림,타부처국유림 17
77.3%
산림청소관국유림 5
 
22.7%

Length

2023-12-12T23:49:34.509941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:49:34.672992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공사유림,타부처국유림 17
77.3%
산림청소관국유림 5
 
22.7%

지역
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T23:49:34.990705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.2727273
Min length3

Characters and Unicode

Total characters116
Distinct characters36
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

Unique22 ?
Unique (%)100.0%

Sample

1st row북부지방청
2nd row동부지방청
3rd row남부지방청
4th row중부지방청
5th row서부지방청
ValueCountFrequency (%)
북부지방청 1
 
4.3%
세종특별자치시 1
 
4.3%
경상남도 1
 
4.3%
경상북도 1
 
4.3%
전라남도 1
 
4.3%
전라북도 1
 
4.3%
충청남도 1
 
4.3%
충청북도 1
 
4.3%
강원도 1
 
4.3%
기도 1
 
4.3%
Other values (13) 13
56.5%
2023-12-12T23:49:35.725777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12
 
10.3%
도 9
 
7.8%
시 8
 
6.9%
청 7
 
6.0%
광 7
 
6.0%
부 6
 
5.2%
역 6
 
5.2%
지 5
 
4.3%
방 5
 
4.3%
북 4
 
3.4%
Other values (26) 47
40.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 104
89.7%
Space Separator 12
 
10.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 9
 
8.7%
시 8
 
7.7%
청 7
 
6.7%
광 7
 
6.7%
부 6
 
5.8%
역 6
 
5.8%
지 5
 
4.8%
방 5
 
4.8%
북 4
 
3.8%
남 4
 
3.8%
Other values (25) 43
41.3%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 104
89.7%
Common 12
 
10.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 9
 
8.7%
시 8
 
7.7%
청 7
 
6.7%
광 7
 
6.7%
부 6
 
5.8%
역 6
 
5.8%
지 5
 
4.8%
방 5
 
4.8%
북 4
 
3.8%
남 4
 
3.8%
Other values (25) 43
41.3%
Common
ValueCountFrequency (%)
12
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 104
89.7%
ASCII 12
 
10.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12
100.0%
Hangul
ValueCountFrequency (%)
도 9
 
8.7%
시 8
 
7.7%
청 7
 
6.7%
광 7
 
6.7%
부 6
 
5.8%
역 6
 
5.8%
지 5
 
4.8%
방 5
 
4.8%
북 4
 
3.8%
남 4
 
3.8%
Other values (25) 43
41.3%

지역영문명
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T23:49:36.018311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length17
Mean length12.909091
Min length5

Characters and Unicode

Total characters284
Distinct characters39
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

Unique22 ?
Unique (%)100.0%

Sample

1st rowNorthern R.S.
2nd rowEastern R.S.
3rd rowSouthern R.S.
4th rowCentral R.S.
5th rowWestern R.S.
ValueCountFrequency (%)
r.s 5
 
15.2%
special 2
 
6.1%
self-governing 2
 
6.1%
northern 1
 
3.0%
jeju 1
 
3.0%
gyeongsangnam-do 1
 
3.0%
gyeongsangbuk-do 1
 
3.0%
jeollanam-do 1
 
3.0%
jeollabuk-do 1
 
3.0%
chungcheongnam-do 1
 
3.0%
Other values (17) 17
51.5%
2023-12-12T23:49:36.438993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 31
 
10.9%
e 27
 
9.5%
o 25
 
8.8%
a 17
 
6.0%
g 16
 
5.6%
S 12
 
4.2%
l 11
 
3.9%
u 11
 
3.9%
11
 
3.9%
- 10
 
3.5%
Other values (29) 113
39.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 213
75.0%
Uppercase Letter 40
 
14.1%
Space Separator 11
 
3.9%
Dash Punctuation 10
 
3.5%
Other Punctuation 10
 
3.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 31
14.6%
e 27
12.7%
o 25
11.7%
a 17
 
8.0%
g 16
 
7.5%
l 11
 
5.2%
u 11
 
5.2%
r 9
 
4.2%
d 8
 
3.8%
h 7
 
3.3%
Other values (13) 51
23.9%
Uppercase Letter
ValueCountFrequency (%)
S 12
30.0%
G 7
17.5%
R 5
12.5%
C 4
 
10.0%
J 3
 
7.5%
D 2
 
5.0%
N 1
 
2.5%
U 1
 
2.5%
I 1
 
2.5%
B 1
 
2.5%
Other values (3) 3
 
7.5%
Space Separator
ValueCountFrequency (%)
11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 253
89.1%
Common 31
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 31
 
12.3%
e 27
 
10.7%
o 25
 
9.9%
a 17
 
6.7%
g 16
 
6.3%
S 12
 
4.7%
l 11
 
4.3%
u 11
 
4.3%
r 9
 
3.6%
d 8
 
3.2%
Other values (26) 86
34.0%
Common
ValueCountFrequency (%)
11
35.5%
- 10
32.3%
. 10
32.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 31
 
10.9%
e 27
 
9.5%
o 25
 
8.8%
a 17
 
6.0%
g 16
 
5.6%
S 12
 
4.2%
l 11
 
3.9%
u 11
 
3.9%
11
 
3.9%
- 10
 
3.5%
Other values (29) 113
39.8%

재해방지보호구역(ha)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.81818
Minimum0
Maximum812
Zeros11
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T23:49:36.609903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q3206
95-th percentile635.6
Maximum812
Range812
Interquartile range (IQR)206

Descriptive statistics

Standard deviation255.63434
Coefficient of variation (CV)1.6405938
Kurtosis1.0634863
Mean155.81818
Median Absolute Deviation (MAD)2
Skewness1.5357051
Sum3428
Variance65348.918
MonotonicityNot monotonic
2023-12-12T23:49:36.763602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 11
50.0%
7 1
 
4.5%
4 1
 
4.5%
507 1
 
4.5%
143 1
 
4.5%
227 1
 
4.5%
51 1
 
4.5%
637 1
 
4.5%
609 1
 
4.5%
367 1
 
4.5%
Other values (2) 2
 
9.1%
ValueCountFrequency (%)
0 11
50.0%
4 1
 
4.5%
7 1
 
4.5%
51 1
 
4.5%
64 1
 
4.5%
143 1
 
4.5%
227 1
 
4.5%
367 1
 
4.5%
507 1
 
4.5%
609 1
 
4.5%
ValueCountFrequency (%)
812 1
4.5%
637 1
4.5%
609 1
4.5%
507 1
4.5%
367 1
4.5%
227 1
4.5%
143 1
4.5%
64 1
4.5%
51 1
4.5%
7 1
4.5%

생활환경보호구역(ha)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size308.0 B
0
20 
3
 
1
10
 
1

Length

Max length2
Median length1
Mean length1.0454545
Min length1

Unique

Unique2 ?
Unique (%)9.1%

Sample

1st row0
2nd row0
3rd row3
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20
90.9%
3 1
 
4.5%
10 1
 
4.5%

Length

2023-12-12T23:49:36.956594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:49:37.105668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20
90.9%
3 1
 
4.5%
10 1
 
4.5%

경관보호구역(ha)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean789.5
Minimum0
Maximum5291
Zeros8
Zeros (%)36.4%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T23:49:37.230962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median99.5
Q31127
95-th percentile2614.7
Maximum5291
Range5291
Interquartile range (IQR)1127

Descriptive statistics

Standard deviation1302.0674
Coefficient of variation (CV)1.6492303
Kurtosis6.1177844
Mean789.5
Median Absolute Deviation (MAD)99.5
Skewness2.3181068
Sum17369
Variance1695379.4
MonotonicityNot monotonic
2023-12-12T23:49:37.409152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 8
36.4%
1162 2
 
9.1%
183 1
 
4.5%
44 1
 
4.5%
822 1
 
4.5%
118 1
 
4.5%
81 1
 
4.5%
16 1
 
4.5%
1022 1
 
4.5%
440 1
 
4.5%
Other values (4) 4
18.2%
ValueCountFrequency (%)
0 8
36.4%
16 1
 
4.5%
44 1
 
4.5%
81 1
 
4.5%
118 1
 
4.5%
183 1
 
4.5%
440 1
 
4.5%
822 1
 
4.5%
1022 1
 
4.5%
1162 2
 
9.1%
ValueCountFrequency (%)
5291 1
4.5%
2627 1
4.5%
2381 1
4.5%
2020 1
4.5%
1162 2
9.1%
1022 1
4.5%
822 1
4.5%
440 1
4.5%
183 1
4.5%
118 1
4.5%

1종수원함양보호구역(ha)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct16
Distinct (%)76.2%
Missing1
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean4264.4762
Minimum0
Maximum19402
Zeros6
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T23:49:37.572159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2163
Q34519
95-th percentile18091
Maximum19402
Range19402
Interquartile range (IQR)4519

Descriptive statistics

Standard deviation5976.4341
Coefficient of variation (CV)1.4014462
Kurtosis1.7892952
Mean4264.4762
Median Absolute Deviation (MAD)2163
Skewness1.6469172
Sum89554
Variance35717764
MonotonicityNot monotonic
2023-12-12T23:49:37.735229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 6
27.3%
2029 1
 
4.5%
2190 1
 
4.5%
18091 1
 
4.5%
13609 1
 
4.5%
755 1
 
4.5%
19402 1
 
4.5%
8811 1
 
4.5%
3816 1
 
4.5%
4519 1
 
4.5%
Other values (6) 6
27.3%
ValueCountFrequency (%)
0 6
27.3%
55 1
 
4.5%
59 1
 
4.5%
755 1
 
4.5%
2029 1
 
4.5%
2163 1
 
4.5%
2174 1
 
4.5%
2190 1
 
4.5%
3816 1
 
4.5%
4322 1
 
4.5%
ValueCountFrequency (%)
19402 1
4.5%
18091 1
4.5%
13609 1
4.5%
8811 1
4.5%
7559 1
4.5%
4519 1
4.5%
4322 1
4.5%
3816 1
4.5%
2190 1
4.5%
2174 1
4.5%

2종수원함양보호구역(ha)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean473.13636
Minimum0
Maximum6062
Zeros14
Zeros (%)63.6%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T23:49:37.890087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3173
95-th percentile2249.2
Maximum6062
Range6062
Interquartile range (IQR)173

Descriptive statistics

Standard deviation1352.9307
Coefficient of variation (CV)2.8594943
Kurtosis15.217105
Mean473.13636
Median Absolute Deviation (MAD)0
Skewness3.7930159
Sum10409
Variance1830421.6
MonotonicityNot monotonic
2023-12-12T23:49:38.021751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 14
63.6%
188 1
 
4.5%
65 1
 
4.5%
961 1
 
4.5%
469 1
 
4.5%
219 1
 
4.5%
6062 1
 
4.5%
2317 1
 
4.5%
128 1
 
4.5%
ValueCountFrequency (%)
0 14
63.6%
65 1
 
4.5%
128 1
 
4.5%
188 1
 
4.5%
219 1
 
4.5%
469 1
 
4.5%
961 1
 
4.5%
2317 1
 
4.5%
6062 1
 
4.5%
ValueCountFrequency (%)
6062 1
 
4.5%
2317 1
 
4.5%
961 1
 
4.5%
469 1
 
4.5%
219 1
 
4.5%
188 1
 
4.5%
128 1
 
4.5%
65 1
 
4.5%
0 14
63.6%

3종수원함양보호구역(ha)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7090
Minimum0
Maximum64519
Zeros11
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T23:49:38.182950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median252
Q36653
95-th percentile36704.9
Maximum64519
Range64519
Interquartile range (IQR)6653

Descriptive statistics

Standard deviation15408.278
Coefficient of variation (CV)2.1732409
Kurtosis9.8171236
Mean7090
Median Absolute Deviation (MAD)252
Skewness3.0716486
Sum155980
Variance2.3741503 × 108
MonotonicityNot monotonic
2023-12-12T23:49:38.338219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 11
50.0%
64519 1
 
4.5%
37979 1
 
4.5%
1769 1
 
4.5%
12497 1
 
4.5%
6608 1
 
4.5%
6668 1
 
4.5%
12214 1
 
4.5%
8498 1
 
4.5%
504 1
 
4.5%
Other values (2) 2
 
9.1%
ValueCountFrequency (%)
0 11
50.0%
504 1
 
4.5%
592 1
 
4.5%
1769 1
 
4.5%
4132 1
 
4.5%
6608 1
 
4.5%
6668 1
 
4.5%
8498 1
 
4.5%
12214 1
 
4.5%
12497 1
 
4.5%
ValueCountFrequency (%)
64519 1
4.5%
37979 1
4.5%
12497 1
4.5%
12214 1
4.5%
8498 1
4.5%
6668 1
4.5%
6608 1
4.5%
4132 1
4.5%
1769 1
4.5%
592 1
4.5%

산림유전자원 보호구역(ha)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7844.8182
Minimum0
Maximum85764
Zeros7
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T23:49:38.489377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median31
Q32596
95-th percentile37716.3
Maximum85764
Range85764
Interquartile range (IQR)2596

Descriptive statistics

Standard deviation20269.887
Coefficient of variation (CV)2.5838568
Kurtosis10.854644
Mean7844.8182
Median Absolute Deviation (MAD)31
Skewness3.2037911
Sum172586
Variance4.1086832 × 108
MonotonicityNot monotonic
2023-12-12T23:49:38.631322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 7
31.8%
8 2
 
9.1%
85764 1
 
4.5%
37944 1
 
4.5%
33390 1
 
4.5%
3201 1
 
4.5%
6011 1
 
4.5%
7 1
 
4.5%
200 1
 
4.5%
781 1
 
4.5%
Other values (5) 5
22.7%
ValueCountFrequency (%)
0 7
31.8%
1 1
 
4.5%
7 1
 
4.5%
8 2
 
9.1%
54 1
 
4.5%
78 1
 
4.5%
141 1
 
4.5%
200 1
 
4.5%
781 1
 
4.5%
3201 1
 
4.5%
ValueCountFrequency (%)
85764 1
4.5%
37944 1
4.5%
33390 1
4.5%
6011 1
4.5%
4998 1
4.5%
3201 1
4.5%
781 1
4.5%
200 1
4.5%
141 1
4.5%
78 1
4.5%

Interactions

2023-12-12T23:49:33.343927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:27.757684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:28.606401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:30.004776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:31.466603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:32.279994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:33.498800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:27.926997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:28.842936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:30.331526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:31.652665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:32.410281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:33.617984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:28.062917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:29.045711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:30.573393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:31.775222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:32.531327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:33.760229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:28.209352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:29.279648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:30.793840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:31.900721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:32.668052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:33.866094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:28.343422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:29.498739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:31.000344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:32.029995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:32.776109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:33.996019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:28.473395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:29.703542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:31.249925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:32.160884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:49:33.234415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:49:38.750297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류지역지역영문명재해방지보호구역(ha)생활환경보호구역(ha)경관보호구역(ha)1종수원함양보호구역(ha)2종수원함양보호구역(ha)3종수원함양보호구역(ha)산림유전자원 보호구역(ha)
분류1.0001.0001.0000.5760.1810.0000.4500.3470.8080.876
지역1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지역영문명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
재해방지보호구역(ha)0.5761.0001.0001.0001.0000.8500.9110.9070.0000.444
생활환경보호구역(ha)0.1811.0001.0001.0001.0000.9770.7200.6310.0000.631
경관보호구역(ha)0.0001.0001.0000.8500.9771.0000.7870.9660.0000.000
1종수원함양보호구역(ha)0.4501.0001.0000.9110.7200.7871.0000.8350.0000.000
2종수원함양보호구역(ha)0.3471.0001.0000.9070.6310.9660.8351.0000.0000.000
3종수원함양보호구역(ha)0.8081.0001.0000.0000.0000.0000.0000.0001.0000.976
산림유전자원 보호구역(ha)0.8761.0001.0000.4440.6310.0000.0000.0000.9761.000
2023-12-12T23:49:38.922116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생활환경보호구역(ha)분류
생활환경보호구역(ha)1.0000.283
분류0.2831.000
2023-12-12T23:49:39.034308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재해방지보호구역(ha)경관보호구역(ha)1종수원함양보호구역(ha)2종수원함양보호구역(ha)3종수원함양보호구역(ha)산림유전자원 보호구역(ha)분류생활환경보호구역(ha)
재해방지보호구역(ha)1.0000.7330.6240.4460.2210.4020.5280.889
경관보호구역(ha)0.7331.0000.8410.6650.3510.5010.0000.743
1종수원함양보호구역(ha)0.6240.8411.0000.4400.3170.3110.4000.557
2종수원함양보호구역(ha)0.4460.6650.4401.0000.3040.6030.2040.631
3종수원함양보호구역(ha)0.2210.3510.3170.3041.0000.6580.5660.000
산림유전자원 보호구역(ha)0.4020.5010.3110.6030.6581.0000.6430.631
분류0.5280.0000.4000.2040.5660.6431.0000.283
생활환경보호구역(ha)0.8890.7430.5570.6310.0000.6310.2831.000

Missing values

2023-12-12T23:49:34.127891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:49:34.324962image/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

분류지역지역영문명재해방지보호구역(ha)생활환경보호구역(ha)경관보호구역(ha)1종수원함양보호구역(ha)2종수원함양보호구역(ha)3종수원함양보호구역(ha)산림유전자원 보호구역(ha)
0산림청소관국유림북부지방청Northern R.S.7018320291886451985764
1산림청소관국유림동부지방청Eastern R.S.40445903797937944
2산림청소관국유림남부지방청Southern R.S.5073822217465176933390
3산림청소관국유림중부지방청Central R.S.143011821630124973201
4산림청소관국유림서부지방청Western R.S.22701162755996166086011
5공사유림,타부처국유림서울특별시Seoul0000000
6공사유림,타부처국유림부산광역시Busan0000000
7공사유림,타부처국유림대구광역시Daegu0000007
8공사유림,타부처국유림인천광역시Incheon0000008
9공사유림,타부처국유림광주광역시Gwangju0000000
분류지역지역영문명재해방지보호구역(ha)생활환경보호구역(ha)경관보호구역(ha)1종수원함양보호구역(ha)2종수원함양보호구역(ha)3종수원함양보호구역(ha)산림유전자원 보호구역(ha)
12공사유림,타부처국유림세종특별자치시Sejong Special Self-Governing City001655000
13공사유림,타부처국유림경 기도Gyeonggi-do00102245194696668200
14공사유림,타부처국유림강원도Gangwon-do00440219021912214781
15공사유림,타부처국유림충청북도Chungcheongbuk-do0003816084981
16공사유림,타부처국유림충청남도Chungcheongnam-do510238188110504141
17공사유림,타부처국유림전라북도Jeollabuk-do6370262719402041320
18공사유림,타부처국유림전라남도Jeollanam-do60902020755606259254
19공사유림,타부처국유림경상북도Gyeongsangbuk-do36710529113609231704998
20공사유림,타부처국유림경상남도Gyeongsangnam-do8120116218091128078
21공사유림,타부처국유림제주특별자치도Jeju Special Self-Governing Province6400<NA>000