Overview

Dataset statistics

Number of variables7
Number of observations100
Missing cells101
Missing cells (%)14.4%
Duplicate rows1
Duplicate rows (%)1.0%
Total size in memory5.7 KiB
Average record size in memory58.3 B

Variable types

Categorical1
Text5
Numeric1

Dataset

Description충청남도내 아름다운 숲 정보(건강한 소나무 숲 조성하기 위해 나무가꾸기, 병해충방제, 토양개량, 피해목 시술, 소나무재선충병 약제주사, 간벌 등 보호·보존사업과 소나무 보존에 해가 되지 않는 범위 내에서 벤치 산책로 등 편익시설 설치, 산림문화공간 및 치유의 숲으로 조성)
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=410&beforeMenuCd=DOM_000000201001001000&publicdatapk=15032216

Alerts

Dataset has 1 (1.0%) duplicate rowsDuplicates
주요명칭 has 32 (32.0%) missing valuesMissing
비고 has 69 (69.0%) missing valuesMissing

Reproduction

Analysis started2024-01-09 21:48:24.084808
Analysis finished2024-01-09 21:48:24.923219
Duration0.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Categorical

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
예 산
10 
아 산
9 
서 산
9 
당 진
9 
부 여
9 
Other values (10)
54 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row천 안
2nd row천 안
3rd row공 주
4th row공 주
5th row공 주

Common Values

ValueCountFrequency (%)
예 산 10
10.0%
아 산 9
9.0%
서 산 9
9.0%
당 진 9
9.0%
부 여 9
9.0%
홍 성 9
9.0%
공 주 8
8.0%
논 산 8
8.0%
청 양 7
7.0%
보 령 6
 
6.0%
Other values (5) 16
16.0%

Length

2024-01-10T06:48:24.969991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
산 41
20.5%
서 12
 
6.0%
예 10
 
5.0%
아 9
 
4.5%
당 9
 
4.5%
진 9
 
4.5%
부 9
 
4.5%
여 9
 
4.5%
홍 9
 
4.5%
성 9
 
4.5%
Other values (13) 74
37.0%

읍 면
Text

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-01-10T06:48:25.137052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.03
Min length2

Characters and Unicode

Total characters203
Distinct characters75
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

Unique36 ?
Unique (%)36.0%

Sample

1st row목천
2nd row직산
3rd row사곡
4th row웅진
5th row사곡
ValueCountFrequency (%)
부적 8
 
8.0%
사곡 4
 
4.0%
송악 4
 
4.0%
안면 4
 
4.0%
화성 3
 
3.0%
덕산 3
 
3.0%
염치 3
 
3.0%
해미 3
 
3.0%
계룡 2
 
2.0%
인지 2
 
2.0%
Other values (50) 64
64.0%
2024-01-10T06:48:25.407644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 11
 
5.4%
산 11
 
5.4%
천 10
 
4.9%
성 8
 
3.9%
적 8
 
3.9%
대 8
 
3.9%
곡 7
 
3.4%
면 6
 
3.0%
미 5
 
2.5%
안 5
 
2.5%
Other values (65) 124
61.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 200
98.5%
Space Separator 3
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 11
 
5.5%
산 11
 
5.5%
천 10
 
5.0%
성 8
 
4.0%
적 8
 
4.0%
대 8
 
4.0%
곡 7
 
3.5%
면 6
 
3.0%
미 5
 
2.5%
안 5
 
2.5%
Other values (64) 121
60.5%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 200
98.5%
Common 3
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 11
 
5.5%
산 11
 
5.5%
천 10
 
5.0%
성 8
 
4.0%
적 8
 
4.0%
대 8
 
4.0%
곡 7
 
3.5%
면 6
 
3.0%
미 5
 
2.5%
안 5
 
2.5%
Other values (64) 121
60.5%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 200
98.5%
ASCII 3
 
1.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 11
 
5.5%
산 11
 
5.5%
천 10
 
5.0%
성 8
 
4.0%
적 8
 
4.0%
대 8
 
4.0%
곡 7
 
3.5%
면 6
 
3.0%
미 5
 
2.5%
안 5
 
2.5%
Other values (64) 121
60.5%
ASCII
ValueCountFrequency (%)
3
100.0%

리 동
Text

Distinct84
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-01-10T06:48:25.611412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.01
Min length1

Characters and Unicode

Total characters201
Distinct characters86
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

Unique77 ?
Unique (%)77.0%

Sample

1st row교촌
2nd row군동
3rd row운암
4th row웅진
5th row가교
ValueCountFrequency (%)
신풍 8
 
8.0%
읍내 5
 
5.0%
교촌 2
 
2.0%
신기 2
 
2.0%
대천 2
 
2.0%
운암 2
 
2.0%
가교 2
 
2.0%
화암 1
 
1.0%
장승 1
 
1.0%
중묵 1
 
1.0%
Other values (74) 74
74.0%
2024-01-10T06:48:25.925003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
신 13
 
6.5%
대 8
 
4.0%
풍 8
 
4.0%
산 7
 
3.5%
양 7
 
3.5%
암 6
 
3.0%
기 6
 
3.0%
천 6
 
3.0%
동 5
 
2.5%
읍 5
 
2.5%
Other values (76) 130
64.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 200
99.5%
Decimal Number 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
신 13
 
6.5%
대 8
 
4.0%
풍 8
 
4.0%
산 7
 
3.5%
양 7
 
3.5%
암 6
 
3.0%
기 6
 
3.0%
천 6
 
3.0%
동 5
 
2.5%
읍 5
 
2.5%
Other values (75) 129
64.5%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 200
99.5%
Common 1
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
신 13
 
6.5%
대 8
 
4.0%
풍 8
 
4.0%
산 7
 
3.5%
양 7
 
3.5%
암 6
 
3.0%
기 6
 
3.0%
천 6
 
3.0%
동 5
 
2.5%
읍 5
 
2.5%
Other values (75) 129
64.5%
Common
ValueCountFrequency (%)
3 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 200
99.5%
ASCII 1
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
신 13
 
6.5%
대 8
 
4.0%
풍 8
 
4.0%
산 7
 
3.5%
양 7
 
3.5%
암 6
 
3.0%
기 6
 
3.0%
천 6
 
3.0%
동 5
 
2.5%
읍 5
 
2.5%
Other values (75) 129
64.5%
ASCII
ValueCountFrequency (%)
3 1
100.0%

번 지
Text

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-01-10T06:48:26.130051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length5.01
Min length2

Characters and Unicode

Total characters501
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)91.0%

Sample

1st row산28-21외2
2nd row산9-1
3rd row산74-2외5
4th row산21-1외1
5th row산38-1외4
ValueCountFrequency (%)
산4 9
 
9.0%
산8-1 1
 
1.0%
산34-1 1
 
1.0%
517-2 1
 
1.0%
879-1 1
 
1.0%
산93-1 1
 
1.0%
산6 1
 
1.0%
산30 1
 
1.0%
산140-1 1
 
1.0%
산208-127외1 1
 
1.0%
Other values (82) 82
82.0%
2024-01-10T06:48:26.452045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 91
18.2%
1 86
17.2%
- 57
11.4%
2 47
9.4%
외 46
9.2%
4 42
8.4%
3 40
8.0%
7 23
 
4.6%
8 17
 
3.4%
5 13
 
2.6%
Other values (6) 39
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 302
60.3%
Other Letter 139
27.7%
Dash Punctuation 57
 
11.4%
Space Separator 3
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 86
28.5%
2 47
15.6%
4 42
13.9%
3 40
13.2%
7 23
 
7.6%
8 17
 
5.6%
5 13
 
4.3%
0 13
 
4.3%
9 12
 
4.0%
6 9
 
3.0%
Other Letter
ValueCountFrequency (%)
산 91
65.5%
외 46
33.1%
대 1
 
0.7%
천 1
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 57
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 362
72.3%
Hangul 139
 
27.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 86
23.8%
- 57
15.7%
2 47
13.0%
4 42
11.6%
3 40
11.0%
7 23
 
6.4%
8 17
 
4.7%
5 13
 
3.6%
0 13
 
3.6%
9 12
 
3.3%
Other values (2) 12
 
3.3%
Hangul
ValueCountFrequency (%)
산 91
65.5%
외 46
33.1%
대 1
 
0.7%
천 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 362
72.3%
Hangul 139
 
27.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 91
65.5%
외 46
33.1%
대 1
 
0.7%
천 1
 
0.7%
ASCII
ValueCountFrequency (%)
1 86
23.8%
- 57
15.7%
2 47
13.0%
4 42
11.6%
3 40
11.0%
7 23
 
6.4%
8 17
 
4.7%
5 13
 
3.6%
0 13
 
3.6%
9 12
 
3.3%
Other values (2) 12
 
3.3%

대상면적(헥타르)
Real number (ℝ)

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.192
Minimum0.1
Maximum156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-10T06:48:26.561602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q12
median3
Q38
95-th percentile35.25
Maximum156
Range155.9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation23.973279
Coefficient of variation (CV)2.3521663
Kurtosis23.740447
Mean10.192
Median Absolute Deviation (MAD)2
Skewness4.749053
Sum1019.2
Variance574.71812
MonotonicityNot monotonic
2024-01-10T06:48:26.668813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
3.0 11
 
11.0%
2.0 11
 
11.0%
5.0 10
 
10.0%
10.0 9
 
9.0%
2.5 8
 
8.0%
4.0 5
 
5.0%
1.5 4
 
4.0%
1.0 4
 
4.0%
0.5 3
 
3.0%
7.0 3
 
3.0%
Other values (23) 32
32.0%
ValueCountFrequency (%)
0.1 2
2.0%
0.2 1
 
1.0%
0.4 1
 
1.0%
0.5 3
3.0%
0.6 2
2.0%
0.7 2
2.0%
0.8 1
 
1.0%
0.9 1
 
1.0%
1.0 4
4.0%
1.2 1
 
1.0%
ValueCountFrequency (%)
156.0 1
 
1.0%
130.0 1
 
1.0%
123.3 1
 
1.0%
50.0 1
 
1.0%
40.0 1
 
1.0%
35.0 1
 
1.0%
25.0 2
2.0%
24.7 1
 
1.0%
20.0 3
3.0%
15.0 3
3.0%

주요명칭
Text

MISSING 

Distinct58
Distinct (%)85.3%
Missing32
Missing (%)32.0%
Memory size932.0 B
2024-01-10T06:48:26.845570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1764706
Min length2

Characters and Unicode

Total characters216
Distinct characters78
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

Unique54 ?
Unique (%)79.4%

Sample

1st row흑성산
2nd row태화산
3rd row곰나루솔밭
4th row능산
5th row철승산자락
ValueCountFrequency (%)
계백장군 8
 
11.8%
방화산 2
 
2.9%
봉화산 2
 
2.9%
남산 2
 
2.9%
덕명산 1
 
1.5%
백월산 1
 
1.5%
흑성산 1
 
1.5%
서광산 1
 
1.5%
금계산 1
 
1.5%
성태산 1
 
1.5%
Other values (48) 48
70.6%
2024-01-10T06:48:27.169448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 56
25.9%
백 10
 
4.6%
계 9
 
4.2%
화 9
 
4.2%
장 8
 
3.7%
군 8
 
3.7%
봉 8
 
3.7%
태 4
 
1.9%
조 4
 
1.9%
성 4
 
1.9%
Other values (68) 96
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 216
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 56
25.9%
백 10
 
4.6%
계 9
 
4.2%
화 9
 
4.2%
장 8
 
3.7%
군 8
 
3.7%
봉 8
 
3.7%
태 4
 
1.9%
조 4
 
1.9%
성 4
 
1.9%
Other values (68) 96
44.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 216
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 56
25.9%
백 10
 
4.6%
계 9
 
4.2%
화 9
 
4.2%
장 8
 
3.7%
군 8
 
3.7%
봉 8
 
3.7%
태 4
 
1.9%
조 4
 
1.9%
성 4
 
1.9%
Other values (68) 96
44.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 216
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 56
25.9%
백 10
 
4.6%
계 9
 
4.2%
화 9
 
4.2%
장 8
 
3.7%
군 8
 
3.7%
봉 8
 
3.7%
태 4
 
1.9%
조 4
 
1.9%
성 4
 
1.9%
Other values (68) 96
44.4%

비고
Text

MISSING 

Distinct16
Distinct (%)51.6%
Missing69
Missing (%)69.0%
Memory size932.0 B
2024-01-10T06:48:27.312871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length6.4193548
Min length3

Characters and Unicode

Total characters199
Distinct characters53
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)41.9%

Sample

1st row2015-12 지정
2nd row마곡사
3rd row봉곡사
4th row외암민속마을
5th row성준경가옥
ValueCountFrequency (%)
계백장군 8
16.3%
2015-12 8
16.3%
지정 8
16.3%
묘소 8
16.3%
덕산 2
 
4.1%
도립공원 2
 
4.1%
봉곡사 1
 
2.0%
외암민속마을 1
 
2.0%
성준경가옥 1
 
2.0%
현충사 1
 
2.0%
Other values (9) 9
18.4%
2024-01-10T06:48:27.551205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18
 
9.0%
1 16
 
8.0%
2 16
 
8.0%
지 9
 
4.5%
계 8
 
4.0%
백 8
 
4.0%
정 8
 
4.0%
- 8
 
4.0%
5 8
 
4.0%
0 8
 
4.0%
Other values (43) 92
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 125
62.8%
Decimal Number 48
 
24.1%
Space Separator 18
 
9.0%
Dash Punctuation 8
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 9
 
7.2%
계 8
 
6.4%
백 8
 
6.4%
정 8
 
6.4%
소 8
 
6.4%
묘 8
 
6.4%
군 8
 
6.4%
장 8
 
6.4%
사 4
 
3.2%
원 4
 
3.2%
Other values (37) 52
41.6%
Decimal Number
ValueCountFrequency (%)
1 16
33.3%
2 16
33.3%
5 8
16.7%
0 8
16.7%
Space Separator
ValueCountFrequency (%)
18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 125
62.8%
Common 74
37.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 9
 
7.2%
계 8
 
6.4%
백 8
 
6.4%
정 8
 
6.4%
소 8
 
6.4%
묘 8
 
6.4%
군 8
 
6.4%
장 8
 
6.4%
사 4
 
3.2%
원 4
 
3.2%
Other values (37) 52
41.6%
Common
ValueCountFrequency (%)
18
24.3%
1 16
21.6%
2 16
21.6%
- 8
10.8%
5 8
10.8%
0 8
10.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 125
62.8%
ASCII 74
37.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18
24.3%
1 16
21.6%
2 16
21.6%
- 8
10.8%
5 8
10.8%
0 8
10.8%
Hangul
ValueCountFrequency (%)
지 9
 
7.2%
계 8
 
6.4%
백 8
 
6.4%
정 8
 
6.4%
소 8
 
6.4%
묘 8
 
6.4%
군 8
 
6.4%
장 8
 
6.4%
사 4
 
3.2%
원 4
 
3.2%
Other values (37) 52
41.6%

Interactions

2024-01-10T06:48:24.642293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:48:27.625849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군읍 면리 동번 지대상면적(헥타르)주요명칭비고
시군1.0001.0000.9880.9950.2880.9960.916
읍 면1.0001.0000.9990.9990.0000.9990.968
리 동0.9880.9991.0001.0000.7760.9990.981
번 지0.9950.9991.0001.0001.0000.9990.986
대상면적(헥타르)0.2880.0000.7761.0001.0000.9780.995
주요명칭0.9960.9990.9990.9990.9781.0001.000
비고0.9160.9680.9810.9860.9951.0001.000
2024-01-10T06:48:27.709764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대상면적(헥타르)시군
대상면적(헥타르)1.0000.122
시군0.1221.000

Missing values

2024-01-10T06:48:24.737076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:48:24.819592image/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.
2024-01-10T06:48:24.888598image/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

시군읍 면리 동번 지대상면적(헥타르)주요명칭비고
0천 안목천교촌산28-21외215.0흑성산<NA>
1천 안직산군동산9-13.0<NA>2015-12 지정
2공 주사곡운암산74-2외550.0태화산마곡사
3공 주웅진웅진산21-1외11.0곰나루솔밭<NA>
4공 주사곡가교산38-1외420.0<NA><NA>
5공 주계룡양화산9-1외115.0능산<NA>
6공 주사곡호계산54외18.0철승산자락<NA>
7공 주신풍봉갑산70-1외110.0<NA><NA>
8공 주계룡하대산32-13.0<NA><NA>
9공 주사곡운암산77-340.0태화산자락<NA>
시군읍 면리 동번 지대상면적(헥타르)주요명칭비고
90예 산덕산사동산14-3외33.0퇴미산<NA>
91예 산봉산대지산31외13.0<NA><NA>
92예 산대흥교촌5383.0<NA><NA>
93예 산덕산시량산14-3외43.0<NA>덕산 도립공원
94예 산덕산사천산4-1외225.0덕숭산덕산 도립공원
95태 안태안상옥산132외13.0백화산<NA>
96태 안안면승언산31-1외3156.0조개산<NA>
97태 안안면중장산50-1외4123.3조남산<NA>
98태 안안면창기산24-12외124.7국사봉<NA>
99태 안안면정당산23-1외3130.0복조산<NA>

Duplicate rows

Most frequently occurring

시군읍 면리 동번 지대상면적(헥타르)주요명칭비고# duplicates
0논 산부적신풍산42.5계백장군계백장군 묘소8