Overview

Dataset statistics

Number of variables15
Number of observations5386
Missing cells351
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory647.1 KiB
Average record size in memory123.0 B

Variable types

DateTime1
Text7
Categorical5
Numeric2

Dataset

Description2022년말 기준 지방자치단체 및 지방산림청에서 지정한 산림유전자원보호구역 현황을 취합한 행정자료- 최신현황 및 구체적인 자료를 해당 지방자치단체 및 지방산림청에 문의/확인 필요
Author산림청
URLhttps://www.data.go.kr/data/15124718/fileData.do

Alerts

완충구역면적(제곱미터) is highly overall correlated with 전이지역면적(제곱미터)High correlation
1차 기관명 is highly overall correlated with 시도High correlation
시도 is highly overall correlated with 1차 기관명High correlation
전이지역면적(제곱미터) is highly overall correlated with 완충구역면적(제곱미터)High correlation
1차 기관명 is highly imbalanced (73.9%)Imbalance
시도 is highly imbalanced (58.6%)Imbalance
지목 is highly imbalanced (98.5%)Imbalance
지정유형 is highly imbalanced (63.5%)Imbalance
전이지역면적(제곱미터) is highly imbalanced (99.7%)Imbalance
리 has 86 (1.6%) missing valuesMissing
지정목적 has 265 (4.9%) missing valuesMissing
완충구역면적(제곱미터) is highly skewed (γ1 = 46.21104433)Skewed
핵심구역면적(제곱미터) has 61 (1.1%) zerosZeros
완충구역면적(제곱미터) has 5258 (97.6%) zerosZeros

Reproduction

Analysis started2023-12-12 06:55:08.948273
Analysis finished2023-12-12 06:55:11.015807
Duration2.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지정일자
Date

Distinct171
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size42.2 KiB
Minimum1978-09-08 00:00:00
Maximum2022-12-19 00:00:00
2023-12-12T15:55:11.095550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:11.263503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

관리번호
Text

Distinct433
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size42.2 KiB
2023-12-12T15:55:11.521681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.9970293
Min length8

Characters and Unicode

Total characters48458
Distinct characters88
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique291 ?
Unique (%)5.4%

Sample

1st row북부춘천-0001
2nd row북부춘천-0002
3rd row북부춘천-0002
4th row북부춘천-0002
5th row북부춘천-0005
ValueCountFrequency (%)
북부민북-0006 2536
47.1%
북부서울-0002 668
 
12.4%
북부서울-0004 380
 
7.1%
북부춘천-0004 268
 
5.0%
북부춘천-0006 151
 
2.8%
북부춘천-0005 119
 
2.2%
북부민북-0003 109
 
2.0%
북부인제-0011 65
 
1.2%
북부민북-0004 62
 
1.2%
북부인제-0016 53
 
1.0%
Other values (423) 975
 
18.1%
2023-12-12T15:55:11.956663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15796
32.6%
북 7344
15.2%
- 5386
 
11.1%
부 5224
 
10.8%
6 2801
 
5.8%
민 2737
 
5.6%
서 1257
 
2.6%
울 1120
 
2.3%
2 873
 
1.8%
4 863
 
1.8%
Other values (78) 5057
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21543
44.5%
Other Letter 21529
44.4%
Dash Punctuation 5386
 
11.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
북 7344
34.1%
부 5224
24.3%
민 2737
 
12.7%
서 1257
 
5.8%
울 1120
 
5.2%
천 664
 
3.1%
춘 612
 
2.8%
남 271
 
1.3%
양 241
 
1.1%
동 207
 
1.0%
Other values (67) 1852
 
8.6%
Decimal Number
ValueCountFrequency (%)
0 15796
73.3%
6 2801
 
13.0%
2 873
 
4.1%
4 863
 
4.0%
1 570
 
2.6%
3 250
 
1.2%
5 205
 
1.0%
7 68
 
0.3%
9 67
 
0.3%
8 50
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 5386
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26929
55.6%
Hangul 21529
44.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
북 7344
34.1%
부 5224
24.3%
민 2737
 
12.7%
서 1257
 
5.8%
울 1120
 
5.2%
천 664
 
3.1%
춘 612
 
2.8%
남 271
 
1.3%
양 241
 
1.1%
동 207
 
1.0%
Other values (67) 1852
 
8.6%
Common
ValueCountFrequency (%)
0 15796
58.7%
- 5386
 
20.0%
6 2801
 
10.4%
2 873
 
3.2%
4 863
 
3.2%
1 570
 
2.1%
3 250
 
0.9%
5 205
 
0.8%
7 68
 
0.3%
9 67
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26929
55.6%
Hangul 21529
44.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15796
58.7%
- 5386
 
20.0%
6 2801
 
10.4%
2 873
 
3.2%
4 863
 
3.2%
1 570
 
2.1%
3 250
 
0.9%
5 205
 
0.8%
7 68
 
0.3%
9 67
 
0.2%
Hangul
ValueCountFrequency (%)
북 7344
34.1%
부 5224
24.3%
민 2737
 
12.7%
서 1257
 
5.8%
울 1120
 
5.2%
천 664
 
3.1%
춘 612
 
2.8%
남 271
 
1.3%
양 241
 
1.1%
동 207
 
1.0%
Other values (67) 1852
 
8.6%

1차 기관명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct16
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size42.2 KiB
북부지방산림청
4562 
동부지방산림청
 
207
서부지방산림청
 
201
남부지방산림청
 
191
중부지방산림청
 
49
Other values (11)
 
176

Length

Max length7
Median length7
Mean length6.9014111
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
북부지방산림청 4562
84.7%
동부지방산림청 207
 
3.8%
서부지방산림청 201
 
3.7%
남부지방산림청 191
 
3.5%
중부지방산림청 49
 
0.9%
전라남도 49
 
0.9%
경상북도 43
 
0.8%
강원도 28
 
0.5%
경상남도 20
 
0.4%
대구광역시 12
 
0.2%
Other values (6) 24
 
0.4%

Length

2023-12-12T15:55:12.098023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
북부지방산림청 4562
84.7%
동부지방산림청 207
 
3.8%
서부지방산림청 201
 
3.7%
남부지방산림청 191
 
3.5%
중부지방산림청 49
 
0.9%
전라남도 49
 
0.9%
경상북도 43
 
0.8%
강원도 28
 
0.5%
경상남도 20
 
0.4%
대구광역시 12
 
0.2%
Other values (6) 24
 
0.4%

2차 기관명
Text

Distinct77
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size42.2 KiB
2023-12-12T15:55:12.320259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.1425919
Min length2

Characters and Unicode

Total characters49242
Distinct characters97
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

Unique15 ?
Unique (%)0.3%

Sample

1st row춘천국유림관리소
2nd row춘천국유림관리소
3rd row춘천국유림관리소
4th row춘천국유림관리소
5th row춘천국유림관리소
ValueCountFrequency (%)
민북지역국유림관리소 3431
63.7%
서울국유림관리소 1056
 
19.6%
영암국유림관리소 84
 
1.6%
양산국유림관리소 69
 
1.3%
양양국유림관리소 60
 
1.1%
난대아열대산림연구소 56
 
1.0%
평창국유림관리소 44
 
0.8%
울진국유림관리소 43
 
0.8%
영주국유림관리소 40
 
0.7%
태백국유림관리소 35
 
0.6%
Other values (68) 472
 
8.8%
2023-12-12T15:55:12.682138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
소 5218
10.6%
림 5216
10.6%
유 5155
10.5%
국 5154
10.5%
관 5139
10.4%
리 5139
10.4%
민 3431
7.0%
북 3431
7.0%
지 3431
7.0%
역 3431
7.0%
Other values (87) 4497
9.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 49238
> 99.9%
Space Separator 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 5218
10.6%
림 5216
10.6%
유 5155
10.5%
국 5154
10.5%
관 5139
10.4%
리 5139
10.4%
민 3431
7.0%
북 3431
7.0%
지 3431
7.0%
역 3431
7.0%
Other values (86) 4493
9.1%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 49238
> 99.9%
Common 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 5218
10.6%
림 5216
10.6%
유 5155
10.5%
국 5154
10.5%
관 5139
10.4%
리 5139
10.4%
민 3431
7.0%
북 3431
7.0%
지 3431
7.0%
역 3431
7.0%
Other values (86) 4493
9.1%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 49238
> 99.9%
ASCII 4
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
소 5218
10.6%
림 5216
10.6%
유 5155
10.5%
국 5154
10.5%
관 5139
10.4%
리 5139
10.4%
민 3431
7.0%
북 3431
7.0%
지 3431
7.0%
역 3431
7.0%
Other values (86) 4493
9.1%
ASCII
ValueCountFrequency (%)
4
100.0%

시도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size42.2 KiB
강원도
3739 
경기도
698 
경기도
 
328
경상북도
 
165
전라남도
 
158
Other values (12)
 
298

Length

Max length7
Median length3
Mean length3.2944671
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강원도 3739
69.4%
경기도 698
 
13.0%
경기도 328
 
6.1%
경상북도 165
 
3.1%
전라남도 158
 
2.9%
경상남도 84
 
1.6%
제주특별자치도 56
 
1.0%
충청북도 37
 
0.7%
인천광역시 32
 
0.6%
전라북도 25
 
0.5%
Other values (7) 64
 
1.2%

Length

2023-12-12T15:55:12.844982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강원도 3739
69.4%
경기도 1026
 
19.0%
경상북도 165
 
3.1%
전라남도 158
 
2.9%
경상남도 84
 
1.6%
제주특별자치도 56
 
1.0%
인천광역시 39
 
0.7%
충청북도 37
 
0.7%
전라북도 25
 
0.5%
충청남도 23
 
0.4%
Other values (5) 34
 
0.6%

시군
Text

Distinct108
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size42.2 KiB
2023-12-12T15:55:13.080225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.2978091
Min length1

Characters and Unicode

Total characters12376
Distinct characters90
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 (%)0.4%

Sample

1st row춘천
2nd row화천
3rd row화천
4th row화천
5th row화천
ValueCountFrequency (%)
철원 1944
36.1%
연천 900
16.7%
양구 755
 
14.0%
인제 583
 
10.8%
화천 203
 
3.8%
파주 106
 
2.0%
신안군 49
 
0.9%
울진군 49
 
0.9%
평창군 47
 
0.9%
고성군 44
 
0.8%
Other values (96) 708
 
13.1%
2023-12-12T15:55:13.442877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 1952
15.8%
철 1945
15.7%
천 1136
9.2%
연 900
 
7.3%
양 854
 
6.9%
구 782
 
6.3%
762
 
6.2%
제 628
 
5.1%
인 583
 
4.7%
군 555
 
4.5%
Other values (80) 2279
18.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11614
93.8%
Space Separator 762
 
6.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 1952
16.8%
철 1945
16.7%
천 1136
9.8%
연 900
7.7%
양 854
7.4%
구 782
 
6.7%
제 628
 
5.4%
인 583
 
5.0%
군 555
 
4.8%
화 270
 
2.3%
Other values (79) 2009
17.3%
Space Separator
ValueCountFrequency (%)
762
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11614
93.8%
Common 762
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 1952
16.8%
철 1945
16.7%
천 1136
9.8%
연 900
7.7%
양 854
7.4%
구 782
 
6.7%
제 628
 
5.4%
인 583
 
5.0%
군 555
 
4.8%
화 270
 
2.3%
Other values (79) 2009
17.3%
Common
ValueCountFrequency (%)
762
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11614
93.8%
ASCII 762
 
6.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 1952
16.8%
철 1945
16.7%
천 1136
9.8%
연 900
7.7%
양 854
7.4%
구 782
 
6.7%
제 628
 
5.4%
인 583
 
5.0%
군 555
 
4.8%
화 270
 
2.3%
Other values (79) 2009
17.3%
ASCII
ValueCountFrequency (%)
762
100.0%

읍면동
Text

Distinct274
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size42.2 KiB
2023-12-12T15:55:13.764655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.1903082
Min length1

Characters and Unicode

Total characters11797
Distinct characters167
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

Unique92 ?
Unique (%)1.7%

Sample

1st row남산
2nd row화천
3rd row화천
4th row화천
5th row상서
ValueCountFrequency (%)
서화 565
 
10.5%
근북 511
 
9.5%
중 503
 
9.3%
원남 488
 
9.0%
방산 487
 
9.0%
근동 383
 
7.1%
해안 231
 
4.3%
김화 167
 
3.1%
근남 162
 
3.0%
백학 137
 
2.5%
Other values (253) 1759
32.6%
2023-12-12T15:55:14.239480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
근 1060
 
9.0%
화 882
 
7.5%
787
 
6.7%
서 763
 
6.5%
남 755
 
6.4%
동 741
 
6.3%
원 687
 
5.8%
면 624
 
5.3%
산 585
 
5.0%
북 576
 
4.9%
Other values (157) 4337
36.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11010
93.3%
Space Separator 787
 
6.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
근 1060
 
9.6%
화 882
 
8.0%
서 763
 
6.9%
남 755
 
6.9%
동 741
 
6.7%
원 687
 
6.2%
면 624
 
5.7%
산 585
 
5.3%
북 576
 
5.2%
중 504
 
4.6%
Other values (156) 3833
34.8%
Space Separator
ValueCountFrequency (%)
787
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11010
93.3%
Common 787
 
6.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
근 1060
 
9.6%
화 882
 
8.0%
서 763
 
6.9%
남 755
 
6.9%
동 741
 
6.7%
원 687
 
6.2%
면 624
 
5.7%
산 585
 
5.3%
북 576
 
5.2%
중 504
 
4.6%
Other values (156) 3833
34.8%
Common
ValueCountFrequency (%)
787
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11010
93.3%
ASCII 787
 
6.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
근 1060
 
9.6%
화 882
 
8.0%
서 763
 
6.9%
남 755
 
6.9%
동 741
 
6.7%
원 687
 
6.2%
면 624
 
5.7%
산 585
 
5.3%
북 576
 
5.2%
중 504
 
4.6%
Other values (156) 3833
34.8%
ASCII
ValueCountFrequency (%)
787
100.0%

리
Text

MISSING 

Distinct501
Distinct (%)9.5%
Missing86
Missing (%)1.6%
Memory size42.2 KiB
2023-12-12T15:55:14.589390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.3558491
Min length1

Characters and Unicode

Total characters12486
Distinct characters200
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

Unique223 ?
Unique (%)4.2%

Sample

1st row방하
2nd row동촌
3rd row동촌
4th row동촌
5th row마현
ValueCountFrequency (%)
율목 310
 
5.8%
도연 263
 
5.0%
진현 249
 
4.7%
가전 223
 
4.2%
건솔 219
 
4.1%
광삼 195
 
3.7%
방통 188
 
3.5%
천미 155
 
2.9%
주파 142
 
2.7%
서화 119
 
2.2%
Other values (456) 3237
61.1%
2023-12-12T15:55:15.072058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1161
 
9.3%
리 737
 
5.9%
현 644
 
5.2%
도 399
 
3.2%
목 316
 
2.5%
율 313
 
2.5%
산 301
 
2.4%
전 287
 
2.3%
연 277
 
2.2%
서 271
 
2.2%
Other values (190) 7780
62.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11325
90.7%
Space Separator 1161
 
9.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 737
 
6.5%
현 644
 
5.7%
도 399
 
3.5%
목 316
 
2.8%
율 313
 
2.8%
산 301
 
2.7%
전 287
 
2.5%
연 277
 
2.4%
서 271
 
2.4%
진 256
 
2.3%
Other values (189) 7524
66.4%
Space Separator
ValueCountFrequency (%)
1161
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11325
90.7%
Common 1161
 
9.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 737
 
6.5%
현 644
 
5.7%
도 399
 
3.5%
목 316
 
2.8%
율 313
 
2.8%
산 301
 
2.7%
전 287
 
2.5%
연 277
 
2.4%
서 271
 
2.4%
진 256
 
2.3%
Other values (189) 7524
66.4%
Common
ValueCountFrequency (%)
1161
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11325
90.7%
ASCII 1161
 
9.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1161
100.0%
Hangul
ValueCountFrequency (%)
리 737
 
6.5%
현 644
 
5.7%
도 399
 
3.5%
목 316
 
2.8%
율 313
 
2.8%
산 301
 
2.7%
전 287
 
2.5%
연 277
 
2.4%
서 271
 
2.4%
진 256
 
2.3%
Other values (189) 7524
66.4%

지목
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.2 KiB
임야
5365 
-
 
14
사적지
 
2
대
 
2
공원
 
1
Other values (2)
 
2

Length

Max length3
Median length2
Mean length1.997215
Min length1

Unique

Unique3 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
임야 5365
99.6%
- 14
 
0.3%
사적지 2
 
< 0.1%
대 2
 
< 0.1%
공원 1
 
< 0.1%
장 1
 
< 0.1%
하천 1
 
< 0.1%

Length

2023-12-12T15:55:15.232373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:55:15.346342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임야 5365
99.6%
14
 
0.3%
사적지 2
 
< 0.1%
대 2
 
< 0.1%
공원 1
 
< 0.1%
장 1
 
< 0.1%
하천 1
 
< 0.1%

지번
Text

Distinct1995
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Memory size42.2 KiB
2023-12-12T15:55:15.729067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length15
Mean length3.9283327
Min length1

Characters and Unicode

Total characters21158
Distinct characters20
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1308 ?
Unique (%)24.3%

Sample

1st row산95-1
2nd row산11
3rd row산11-11
4th row산304-7
5th row산12
ValueCountFrequency (%)
산1 92
 
1.7%
산1-1 45
 
0.8%
산3 41
 
0.8%
산6 32
 
0.6%
산11 31
 
0.6%
산4 30
 
0.6%
산2 29
 
0.5%
산13 29
 
0.5%
산10 28
 
0.5%
산9 27
 
0.5%
Other values (1981) 5011
92.9%
2023-12-12T15:55:16.218292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 4649
22.0%
1 3677
17.4%
2 2139
10.1%
3 1595
 
7.5%
- 1427
 
6.7%
4 1367
 
6.5%
5 1242
 
5.9%
6 1138
 
5.4%
7 1032
 
4.9%
9 1005
 
4.7%
Other values (10) 1887
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15039
71.1%
Other Letter 4663
 
22.0%
Dash Punctuation 1427
 
6.7%
Space Separator 20
 
0.1%
Other Punctuation 9
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3677
24.4%
2 2139
14.2%
3 1595
10.6%
4 1367
 
9.1%
5 1242
 
8.3%
6 1138
 
7.6%
7 1032
 
6.9%
9 1005
 
6.7%
8 924
 
6.1%
0 920
 
6.1%
Other Letter
ValueCountFrequency (%)
산 4649
99.7%
외 3
 
0.1%
임 3
 
0.1%
필 3
 
0.1%
지 3
 
0.1%
등 1
 
< 0.1%
신 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 1427
100.0%
Space Separator
ValueCountFrequency (%)
20
100.0%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16495
78.0%
Hangul 4663
 
22.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3677
22.3%
2 2139
13.0%
3 1595
9.7%
- 1427
 
8.7%
4 1367
 
8.3%
5 1242
 
7.5%
6 1138
 
6.9%
7 1032
 
6.3%
9 1005
 
6.1%
8 924
 
5.6%
Other values (3) 949
 
5.8%
Hangul
ValueCountFrequency (%)
산 4649
99.7%
외 3
 
0.1%
임 3
 
0.1%
필 3
 
0.1%
지 3
 
0.1%
등 1
 
< 0.1%
신 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16495
78.0%
Hangul 4663
 
22.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 4649
99.7%
외 3
 
0.1%
임 3
 
0.1%
필 3
 
0.1%
지 3
 
0.1%
등 1
 
< 0.1%
신 1
 
< 0.1%
ASCII
ValueCountFrequency (%)
1 3677
22.3%
2 2139
13.0%
3 1595
9.7%
- 1427
 
8.7%
4 1367
 
8.3%
5 1242
 
7.5%
6 1138
 
6.9%
7 1032
 
6.3%
9 1005
 
6.1%
8 924
 
5.6%
Other values (3) 949
 
5.8%

지정유형
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.2 KiB
자연생태계보전지역
4487 
희귀식물자생지
 
315
원시림
 
241
산림습지및산림내계곡천
 
145
진귀한임상
 
96
Other values (2)
 
102

Length

Max length11
Median length9
Mean length8.556257
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row원시림
2nd row희귀식물자생지
3rd row희귀식물자생지
4th row희귀식물자생지
5th row원시림

Common Values

ValueCountFrequency (%)
자연생태계보전지역 4487
83.3%
희귀식물자생지 315
 
5.8%
원시림 241
 
4.5%
산림습지및산림내계곡천 145
 
2.7%
진귀한임상 96
 
1.8%
유용식물자생지 86
 
1.6%
고산식물지대 16
 
0.3%

Length

2023-12-12T15:55:16.353919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:55:16.458733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자연생태계보전지역 4487
83.3%
희귀식물자생지 315
 
5.8%
원시림 241
 
4.5%
산림습지및산림내계곡천 145
 
2.7%
진귀한임상 96
 
1.8%
유용식물자생지 86
 
1.6%
고산식물지대 16
 
0.3%

지정목적
Text

MISSING 

Distinct76
Distinct (%)1.5%
Missing265
Missing (%)4.9%
Memory size42.2 KiB
2023-12-12T15:55:16.668283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length80
Median length9
Mean length9.4016794
Min length3

Characters and Unicode

Total characters48146
Distinct characters188
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.5%

Sample

1st row원시림
2nd row희귀식물 자생지
3rd row희귀식물 자생지
4th row희귀식물 자생지
5th row원시림
ValueCountFrequency (%)
자연생태계보전지역 2916
38.2%
자연생태계 1332
17.4%
보전지역 1329
17.4%
보호 387
 
5.1%
산림유전자원 207
 
2.7%
및 134
 
1.8%
희귀식물 127
 
1.7%
원시림 122
 
1.6%
해당임상 74
 
1.0%
유지·증진 72
 
0.9%
Other values (172) 941
 
12.3%
2023-12-12T15:55:17.030272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
보 4790
9.9%
자 4589
9.5%
전 4566
9.5%
지 4537
9.4%
생 4440
9.2%
태 4399
9.1%
계 4358
9.1%
연 4289
8.9%
역 4262
8.9%
2521
5.2%
Other values (178) 5395
11.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 45437
94.4%
Space Separator 2521
 
5.2%
Other Punctuation 173
 
0.4%
Close Punctuation 6
 
< 0.1%
Open Punctuation 6
 
< 0.1%
Decimal Number 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
보 4790
10.5%
자 4589
10.1%
전 4566
10.0%
지 4537
10.0%
생 4440
9.8%
태 4399
9.7%
계 4358
9.6%
연 4289
9.4%
역 4262
9.4%
림 536
 
1.2%
Other values (169) 4671
10.3%
Other Punctuation
ValueCountFrequency (%)
· 166
96.0%
, 6
 
3.5%
. 1
 
0.6%
Decimal Number
ValueCountFrequency (%)
7 1
33.3%
1 1
33.3%
5 1
33.3%
Space Separator
ValueCountFrequency (%)
2521
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 45437
94.4%
Common 2709
 
5.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
보 4790
10.5%
자 4589
10.1%
전 4566
10.0%
지 4537
10.0%
생 4440
9.8%
태 4399
9.7%
계 4358
9.6%
연 4289
9.4%
역 4262
9.4%
림 536
 
1.2%
Other values (169) 4671
10.3%
Common
ValueCountFrequency (%)
2521
93.1%
· 166
 
6.1%
) 6
 
0.2%
( 6
 
0.2%
, 6
 
0.2%
. 1
 
< 0.1%
7 1
 
< 0.1%
1 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 45437
94.4%
ASCII 2543
 
5.3%
None 166
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
보 4790
10.5%
자 4589
10.1%
전 4566
10.0%
지 4537
10.0%
생 4440
9.8%
태 4399
9.7%
계 4358
9.6%
연 4289
9.4%
역 4262
9.4%
림 536
 
1.2%
Other values (169) 4671
10.3%
ASCII
ValueCountFrequency (%)
2521
99.1%
) 6
 
0.2%
( 6
 
0.2%
, 6
 
0.2%
. 1
 
< 0.1%
7 1
 
< 0.1%
1 1
 
< 0.1%
5 1
 
< 0.1%
None
ValueCountFrequency (%)
· 166
100.0%

핵심구역면적(제곱미터)
Real number (ℝ)

ZEROS 

Distinct3949
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean296853.29
Minimum0
Maximum53197063
Zeros61
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size47.5 KiB
2023-12-12T15:55:17.198803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile347.25
Q13422.5
median13270
Q342334
95-th percentile720000
Maximum53197063
Range53197063
Interquartile range (IQR)38911.5

Descriptive statistics

Standard deviation2091439.5
Coefficient of variation (CV)7.045364
Kurtosis305.61109
Mean296853.29
Median Absolute Deviation (MAD)11883.5
Skewness15.588386
Sum1.5988518 × 109
Variance4.374119 × 1012
MonotonicityNot monotonic
2023-12-12T15:55:17.375058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 61
 
1.1%
496 20
 
0.4%
595 15
 
0.3%
397 15
 
0.3%
10000 14
 
0.3%
2975 14
 
0.3%
3967 13
 
0.2%
198 13
 
0.2%
5950 13
 
0.2%
7934 13
 
0.2%
Other values (3939) 5195
96.5%
ValueCountFrequency (%)
0 61
1.1%
1 1
 
< 0.1%
3 1
 
< 0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
18 2
 
< 0.1%
23 1
 
< 0.1%
25 1
 
< 0.1%
26 1
 
< 0.1%
27 1
 
< 0.1%
ValueCountFrequency (%)
53197063 1
< 0.1%
49188808 1
< 0.1%
48190000 1
< 0.1%
46841280 1
< 0.1%
40728467 1
< 0.1%
36739644 1
< 0.1%
33700000 1
< 0.1%
29529117 1
< 0.1%
22850000 1
< 0.1%
21963033 1
< 0.1%

완충구역면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct128
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20829.795
Minimum0
Maximum29958357
Zeros5258
Zeros (%)97.6%
Negative0
Negative (%)0.0%
Memory size47.5 KiB
2023-12-12T15:55:17.561141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum29958357
Range29958357
Interquartile range (IQR)0

Descriptive statistics

Standard deviation493268.66
Coefficient of variation (CV)23.680918
Kurtosis2598.7931
Mean20829.795
Median Absolute Deviation (MAD)0
Skewness46.211044
Sum1.1218928 × 108
Variance2.4331398 × 1011
MonotonicityNot monotonic
2023-12-12T15:55:17.760368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5258
97.6%
3000 2
 
< 0.1%
56400 1
 
< 0.1%
78001 1
 
< 0.1%
72512 1
 
< 0.1%
50199 1
 
< 0.1%
67670 1
 
< 0.1%
1614745 1
 
< 0.1%
64000 1
 
< 0.1%
2730000 1
 
< 0.1%
Other values (118) 118
 
2.2%
ValueCountFrequency (%)
0 5258
97.6%
1044 1
 
< 0.1%
1133 1
 
< 0.1%
1823 1
 
< 0.1%
2020 1
 
< 0.1%
2876 1
 
< 0.1%
2898 1
 
< 0.1%
2935 1
 
< 0.1%
3000 2
 
< 0.1%
4134 1
 
< 0.1%
ValueCountFrequency (%)
29958357 1
< 0.1%
10627192 1
< 0.1%
8349155 1
< 0.1%
7623930 1
< 0.1%
6496569 1
< 0.1%
6421950 1
< 0.1%
4460276 1
< 0.1%
4382918 1
< 0.1%
3029396 1
< 0.1%
2730000 1
< 0.1%

전이지역면적(제곱미터)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.2 KiB
0
5385 
3744347
 
1

Length

Max length7
Median length1
Mean length1.001114
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 5385
> 99.9%
3744347 1
 
< 0.1%

Length

2023-12-12T15:55:17.957832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:55:18.089498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5385
> 99.9%
3744347 1
 
< 0.1%

Interactions

2023-12-12T15:55:10.478742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:10.330627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:10.560813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:10.403171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:55:18.182050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1차 기관명2차 기관명시도지목지정유형지정목적핵심구역면적(제곱미터)완충구역면적(제곱미터)전이지역면적(제곱미터)
1차 기관명1.0001.0000.9480.6240.7110.9980.1480.0000.182
2차 기관명1.0001.0000.9910.9220.8960.9980.4990.6041.000
시도0.9480.9911.0000.5820.6510.9700.0970.0000.060
지목0.6240.9220.5821.0000.2710.8500.0000.0000.000
지정유형0.7110.8960.6510.2711.0000.9540.1650.0290.040
지정목적0.9980.9980.9700.8500.9541.0000.5460.6681.000
핵심구역면적(제곱미터)0.1480.4990.0970.0000.1650.5461.0000.4010.488
완충구역면적(제곱미터)0.0000.6040.0000.0000.0290.6680.4011.0000.475
전이지역면적(제곱미터)0.1821.0000.0600.0000.0401.0000.4880.4751.000
2023-12-12T15:55:18.324376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정유형시도지목1차 기관명전이지역면적(제곱미터)
지정유형1.0000.3630.0970.4250.043
시도0.3631.0000.3080.6990.054
지목0.0970.3081.0000.3450.000
1차 기관명0.4250.6990.3451.0000.142
전이지역면적(제곱미터)0.0430.0540.0000.1421.000
2023-12-12T15:55:18.461302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
핵심구역면적(제곱미터)완충구역면적(제곱미터)1차 기관명시도지목지정유형전이지역면적(제곱미터)
핵심구역면적(제곱미터)1.000-0.0730.0580.0380.0000.0840.376
완충구역면적(제곱미터)-0.0731.0000.0000.0000.0000.0190.577
1차 기관명0.0580.0001.0000.6990.3450.4250.142
시도0.0380.0000.6991.0000.3080.3630.054
지목0.0000.0000.3450.3081.0000.0970.000
지정유형0.0840.0190.4250.3630.0971.0000.043
전이지역면적(제곱미터)0.3760.5770.1420.0540.0000.0431.000

Missing values

2023-12-12T15:55:10.678632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:55:10.852078image/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.
2023-12-12T15:55:10.970060image/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

지정일자관리번호1차 기관명2차 기관명시도시군읍면동리지목지번지정유형지정목적핵심구역면적(제곱미터)완충구역면적(제곱미터)전이지역면적(제곱미터)
02001-07-20북부춘천-0001북부지방산림청춘천국유림관리소강원도춘천남산방하임야산95-1원시림원시림7000000
12006-11-09북부춘천-0002북부지방산림청춘천국유림관리소강원도화천화천동촌임야산11희귀식물자생지희귀식물 자생지2952911700
22006-11-09북부춘천-0002북부지방산림청춘천국유림관리소강원도화천화천동촌임야산11-11희귀식물자생지희귀식물 자생지226407300
32006-11-09북부춘천-0002북부지방산림청춘천국유림관리소강원도화천화천동촌임야산304-7희귀식물자생지희귀식물 자생지273059900
42006-12-06북부춘천-0005북부지방산림청춘천국유림관리소강원도화천상서마현임야산12원시림원시림2327900
52006-12-11북부춘천-0006북부지방산림청춘천국유림관리소강원도화천화천풍산임야1431-1자연생태계보전지역자연생태계 보전지역1800
62006-12-11북부춘천-0006북부지방산림청춘천국유림관리소강원도화천화천풍산임야산267자연생태계보전지역자연생태계 보전지역1016868000
72006-12-11북부춘천-0006북부지방산림청춘천국유림관리소강원도화천화천풍산임야산344자연생태계보전지역자연생태계 보전지역2468500
82008-12-29북부춘천-0008북부지방산림청춘천국유림관리소강원도화천상서산양임야산106자연생태계보전지역자연생태계 보전지역0130710
92008-12-29북부춘천-0008북부지방산림청춘천국유림관리소강원도화천상서산양임야산107자연생태계보전지역자연생태계 보전지역8974691630
지정일자관리번호1차 기관명2차 기관명시도시군읍면동리지목지번지정유형지정목적핵심구역면적(제곱미터)완충구역면적(제곱미터)전이지역면적(제곱미터)
53761982-11-10경남남해-0001경상남도남해군경상남도남해군미조면송정리임야1136-4진귀한임상해수욕장1400
53772014-02-13경남함양-0001경상남도함양군경상남도함양안의면상원리임야산156-1희귀식물자생지백작약등 희귀식물 보호4000000
53781982-11-10경남거창-0001경상남도거창군경상남도거창군웅양면동호리임야1069원시림풍치림 보존2075000
53791982-11-10경남거창-0002경상남도거창군경상남도거창군북상면농산리대361원시림풍치림 보존129100
53801982-11-10경남거창-0002경상남도거창군경상남도거창군북상면농산리임야361-1원시림풍치림 보존1436500
53811982-11-10경남거창-0003경상남도거창군경상남도거창군위천면황산리대769원시림풍치림 보존4000000
53821982-11-10경남거창-0004경상남도거창군경상남도거창군남하면양항리임야1048원시림풍치림 보존358300
53831982-11-10경남거창-0005경상남도거창군경상남도거창군가북면용산리임야377원시림풍치림 보존1121300
53841982-11-10경남거창-0005경상남도거창군경상남도거창군가북면용산리임야377-1원시림풍치림 보존301500
53852013-12-26경남거창-0006경상남도거창군경상남도거창군위천면상천리임야산61-1희귀식물자생지희귀식물보존4000000