Overview

Dataset statistics

Number of variables24
Number of observations576
Missing cells649
Missing cells (%)4.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory110.9 KiB
Average record size in memory197.2 B

Variable types

Categorical12
Text5
DateTime2
Numeric5

Alerts

시도명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
보호수유형명 is highly imbalanced (96.7%)Imbalance
과명 is highly imbalanced (55.2%)Imbalance
학명 is highly imbalanced (58.5%)Imbalance
나무종류 is highly imbalanced (58.5%)Imbalance
보호수해지일자 has 556 (96.5%) missing valuesMissing
지적 has 93 (16.1%) missing valuesMissing
지적 is highly skewed (γ1 = 21.77853805)Skewed
그루수 has 21 (3.6%) zerosZeros

Reproduction

Analysis started2024-03-14 01:27:52.267236
Analysis finished2024-03-14 01:27:52.800715
Duration0.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
전라북도
576 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라북도 576
100.0%

Length

2024-03-14T10:27:52.853796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:27:52.932910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라북도 576
100.0%

시군구명
Categorical

Distinct14
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
남원시
74 
순창군
74 
고창군
56 
진안군
55 
무주군
49 
Other values (9)
268 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남원시 74
12.8%
순창군 74
12.8%
고창군 56
9.7%
진안군 55
9.5%
무주군 49
8.5%
완주군 48
8.3%
김제시 44
7.6%
정읍시 43
7.5%
장수군 37
6.4%
전주시 28
 
4.9%
Other values (4) 68
11.8%

Length

2024-03-14T10:27:53.006501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
남원시 74
12.8%
순창군 74
12.8%
고창군 56
9.7%
진안군 55
9.5%
무주군 49
8.5%
완주군 48
8.3%
김제시 44
7.6%
정읍시 43
7.5%
장수군 37
6.4%
전주시 28
 
4.9%
Other values (4) 68
11.8%

관리기관명
Categorical

Distinct14
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
남원시
74 
순창군
74 
고창군
56 
진안군
55 
무주군
49 
Other values (9)
268 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남원시 74
12.8%
순창군 74
12.8%
고창군 56
9.7%
진안군 55
9.5%
무주군 49
8.5%
완주군 48
8.3%
김제시 44
7.6%
정읍시 43
7.5%
장수군 37
6.4%
전주시 28
 
4.9%
Other values (4) 68
11.8%

Length

2024-03-14T10:27:53.100827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
남원시 74
12.8%
순창군 74
12.8%
고창군 56
9.7%
진안군 55
9.5%
무주군 49
8.5%
완주군 48
8.3%
김제시 44
7.6%
정읍시 43
7.5%
장수군 37
6.4%
전주시 28
 
4.9%
Other values (4) 68
11.8%

지정번호
Text

Distinct572
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2024-03-14T10:27:53.387285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length7.2100694
Min length3

Characters and Unicode

Total characters4153
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique568 ?
Unique (%)98.6%

Sample

1st row9-1
2nd row9-1-1
3rd row9-1-3
4th row9-1-4
5th row9-1-5
ValueCountFrequency (%)
9-16-8-1-1 2
 
0.3%
9-4-20 2
 
0.3%
9-6-10 2
 
0.3%
9-4-27 2
 
0.3%
9-9-20 1
 
0.2%
9-10-1-1 1
 
0.2%
9-1 1
 
0.2%
9-9-16 1
 
0.2%
9-9-17 1
 
0.2%
9-9-18 1
 
0.2%
Other values (562) 562
97.6%
2024-03-14T10:27:53.780859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1509
36.3%
1 704
17.0%
9 659
15.9%
2 267
 
6.4%
4 186
 
4.5%
6 176
 
4.2%
3 168
 
4.0%
5 151
 
3.6%
8 134
 
3.2%
7 122
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2644
63.7%
Dash Punctuation 1509
36.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 704
26.6%
9 659
24.9%
2 267
 
10.1%
4 186
 
7.0%
6 176
 
6.7%
3 168
 
6.4%
5 151
 
5.7%
8 134
 
5.1%
7 122
 
4.6%
0 77
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 1509
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4153
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1509
36.3%
1 704
17.0%
9 659
15.9%
2 267
 
6.4%
4 186
 
4.5%
6 176
 
4.2%
3 168
 
4.0%
5 151
 
3.6%
8 134
 
3.2%
7 122
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4153
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1509
36.3%
1 704
17.0%
9 659
15.9%
2 267
 
6.4%
4 186
 
4.5%
6 176
 
4.2%
3 168
 
4.0%
5 151
 
3.6%
8 134
 
3.2%
7 122
 
2.9%

보호수지정일자
Date

Distinct89
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
Minimum1982-09-20 00:00:00
Maximum2017-10-26 00:00:00
2024-03-14T10:27:53.901941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:27:54.017108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

보호수해지일자
Date

MISSING 

Distinct14
Distinct (%)70.0%
Missing556
Missing (%)96.5%
Memory size4.6 KiB
Minimum2007-06-28 00:00:00
Maximum2017-08-04 00:00:00
2024-03-14T10:27:54.106996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:27:54.187508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)

보호수유형명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
노목
574 
희귀목
 
2

Length

Max length3
Median length2
Mean length2.0034722
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
노목 574
99.7%
희귀목 2
 
0.3%

Length

2024-03-14T10:27:54.284341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:27:54.355854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노목 574
99.7%
희귀목 2
 
0.3%

과명
Categorical

IMBALANCE 

Distinct16
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
느티나무과
387 
소나무과
53 
느릅나무과
51 
은행나무과
 
32
버드나무과
 
27
Other values (11)
 
26

Length

Max length6
Median length5
Mean length4.8541667
Min length2

Unique

Unique5 ?
Unique (%)0.9%

Sample

1st row은행나무과
2nd row먹구슬과
3rd row은행나무과
4th row은행나무과
5th row은행나무과

Common Values

ValueCountFrequency (%)
느티나무과 387
67.2%
소나무과 53
 
9.2%
느릅나무과 51
 
8.9%
은행나무과 32
 
5.6%
버드나무과 27
 
4.7%
콩과 6
 
1.0%
부처꽃과 6
 
1.0%
참나무과 3
 
0.5%
측백나무과 2
 
0.3%
장미과 2
 
0.3%
Other values (6) 7
 
1.2%

Length

2024-03-14T10:27:54.443303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
느티나무과 387
67.2%
소나무과 53
 
9.2%
느릅나무과 51
 
8.9%
은행나무과 32
 
5.6%
버드나무과 27
 
4.7%
콩과 6
 
1.0%
부처꽃과 6
 
1.0%
참나무과 3
 
0.5%
측백나무과 2
 
0.3%
장미과 2
 
0.3%
Other values (6) 7
 
1.2%

학명
Categorical

IMBALANCE 

Distinct24
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
Zelkova serrata
387 
Pinus densiflora
50 
Celtis sinensis Persoon
49 
Ginkgo biloba
 
32
Salix koreensis ANDERSS.
 
21
Other values (19)
 
37

Length

Max length42
Median length15
Mean length16.453125
Min length13

Unique

Unique12 ?
Unique (%)2.1%

Sample

1st rowGinkgo biloba
2nd rowCedrela sinensis
3rd rowGinkgo biloba
4th rowGinkgo biloba
5th rowGinkgo biloba

Common Values

ValueCountFrequency (%)
Zelkova serrata 387
67.2%
Pinus densiflora 50
 
8.7%
Celtis sinensis Persoon 49
 
8.5%
Ginkgo biloba 32
 
5.6%
Salix koreensis ANDERSS. 21
 
3.6%
Salix chaenomeloides Kimura 6
 
1.0%
Lagerstroemia indica 6
 
1.0%
Sophora japonica 5
 
0.9%
Ulmus davidiana var. japonica 2
 
0.3%
Quercus acutissima 2
 
0.3%
Other values (14) 16
 
2.8%

Length

2024-03-14T10:27:54.565703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
zelkova 387
31.0%
serrata 387
31.0%
pinus 51
 
4.1%
sinensis 51
 
4.1%
densiflora 50
 
4.0%
celtis 49
 
3.9%
persoon 49
 
3.9%
ginkgo 32
 
2.6%
biloba 32
 
2.6%
salix 27
 
2.2%
Other values (46) 133
 
10.7%

나무종류
Categorical

IMBALANCE 

Distinct24
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
느티나무
387 
소나무
50 
팽나무
49 
은행나무
 
32
버드나무
 
21
Other values (19)
 
37

Length

Max length6
Median length4
Mean length3.8333333
Min length2

Unique

Unique12 ?
Unique (%)2.1%

Sample

1st row은행나무
2nd row참죽나무
3rd row은행나무
4th row은행나무
5th row은행나무

Common Values

ValueCountFrequency (%)
느티나무 387
67.2%
소나무 50
 
8.7%
팽나무 49
 
8.5%
은행나무 32
 
5.6%
버드나무 21
 
3.6%
왕버들나무 6
 
1.0%
배롱나무 6
 
1.0%
회화나무 5
 
0.9%
느릅나무 2
 
0.3%
상수리나무 2
 
0.3%
Other values (14) 16
 
2.8%

Length

2024-03-14T10:27:54.667816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
느티나무 387
67.2%
소나무 50
 
8.7%
팽나무 49
 
8.5%
은행나무 32
 
5.6%
버드나무 21
 
3.6%
왕버들나무 6
 
1.0%
배롱나무 6
 
1.0%
회화나무 5
 
0.9%
향나무 2
 
0.3%
서나무 2
 
0.3%
Other values (14) 16
 
2.8%

그루수
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1215278
Minimum0
Maximum7
Zeros21
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2024-03-14T10:27:54.760284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.61784064
Coefficient of variation (CV)0.55089196
Kurtosis32.554719
Mean1.1215278
Median Absolute Deviation (MAD)0
Skewness4.4911856
Sum646
Variance0.38172705
MonotonicityNot monotonic
2024-03-14T10:27:54.930344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 497
86.3%
2 38
 
6.6%
0 21
 
3.6%
3 13
 
2.3%
4 5
 
0.9%
7 2
 
0.3%
ValueCountFrequency (%)
0 21
 
3.6%
1 497
86.3%
2 38
 
6.6%
3 13
 
2.3%
4 5
 
0.9%
7 2
 
0.3%
ValueCountFrequency (%)
7 2
 
0.3%
4 5
 
0.9%
3 13
 
2.3%
2 38
 
6.6%
1 497
86.3%
0 21
 
3.6%

나무나이
Categorical

Distinct48
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
300
123 
200
77 
500
42 
400
42 
350
39 
Other values (43)
253 

Length

Max length7
Median length3
Mean length3.0052083
Min length2

Unique

Unique14 ?
Unique (%)2.4%

Sample

1st row580
2nd row350
3rd row380
4th row350
5th row350

Common Values

ValueCountFrequency (%)
300 123
21.4%
200 77
13.4%
500 42
 
7.3%
400 42
 
7.3%
350 39
 
6.8%
250 38
 
6.6%
150 23
 
4.0%
450 19
 
3.3%
280 17
 
3.0%
410 12
 
2.1%
Other values (38) 144
25.0%

Length

2024-03-14T10:27:55.063549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
300 123
21.4%
200 77
13.4%
500 42
 
7.3%
400 42
 
7.3%
350 39
 
6.8%
250 38
 
6.6%
150 23
 
4.0%
450 19
 
3.3%
280 17
 
3.0%
410 12
 
2.1%
Other values (38) 144
25.0%

나무높이
Real number (ℝ)

Distinct36
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.996875
Minimum0
Maximum340
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2024-03-14T10:27:55.161106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q115
median18
Q320
95-th percentile29
Maximum340
Range340
Interquartile range (IQR)5

Descriptive statistics

Standard deviation16.382713
Coefficient of variation (CV)0.86238988
Kurtosis282.25605
Mean18.996875
Median Absolute Deviation (MAD)3
Skewness15.478204
Sum10942.2
Variance268.39328
MonotonicityNot monotonic
2024-03-14T10:27:55.279250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
20.0 109
18.9%
15.0 84
14.6%
25.0 53
 
9.2%
18.0 51
 
8.9%
16.0 36
 
6.2%
12.0 28
 
4.9%
19.0 23
 
4.0%
17.0 22
 
3.8%
10.0 19
 
3.3%
13.0 17
 
3.0%
Other values (26) 134
23.3%
ValueCountFrequency (%)
0.0 1
 
0.2%
3.2 1
 
0.2%
5.0 2
 
0.3%
6.0 6
 
1.0%
6.5 1
 
0.2%
7.0 3
 
0.5%
7.5 1
 
0.2%
8.0 12
2.1%
10.0 19
3.3%
11.0 11
1.9%
ValueCountFrequency (%)
340.0 1
 
0.2%
200.0 1
 
0.2%
38.0 2
 
0.3%
37.0 1
 
0.2%
35.0 4
 
0.7%
33.0 1
 
0.2%
31.0 2
 
0.3%
30.0 16
2.8%
29.0 3
 
0.5%
28.0 5
 
0.9%

가슴높이둘레
Text

Distinct94
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2024-03-14T10:27:55.497042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.9965278
Min length1

Characters and Unicode

Total characters1726
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)5.9%

Sample

1st row420
2nd row350
3rd row600
4th row550
5th row550
ValueCountFrequency (%)
400 46
 
8.0%
500 44
 
7.6%
300 34
 
5.9%
450 30
 
5.2%
600 26
 
4.5%
350 20
 
3.5%
200 17
 
3.0%
430 16
 
2.8%
250 16
 
2.8%
650 15
 
2.6%
Other values (84) 312
54.2%
2024-03-14T10:27:55.814941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 749
43.4%
5 199
 
11.5%
4 184
 
10.7%
3 164
 
9.5%
2 113
 
6.5%
6 104
 
6.0%
8 71
 
4.1%
1 57
 
3.3%
7 55
 
3.2%
9 28
 
1.6%
Other values (2) 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1724
99.9%
Other Punctuation 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 749
43.4%
5 199
 
11.5%
4 184
 
10.7%
3 164
 
9.5%
2 113
 
6.6%
6 104
 
6.0%
8 71
 
4.1%
1 57
 
3.3%
7 55
 
3.2%
9 28
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1726
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 749
43.4%
5 199
 
11.5%
4 184
 
10.7%
3 164
 
9.5%
2 113
 
6.5%
6 104
 
6.0%
8 71
 
4.1%
1 57
 
3.3%
7 55
 
3.2%
9 28
 
1.6%
Other values (2) 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 749
43.4%
5 199
 
11.5%
4 184
 
10.7%
3 164
 
9.5%
2 113
 
6.5%
6 104
 
6.0%
8 71
 
4.1%
1 57
 
3.3%
7 55
 
3.2%
9 28
 
1.6%
Other values (2) 2
 
0.1%

나무갓지름
Text

Distinct94
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2024-03-14T10:27:56.054767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.9965278
Min length1

Characters and Unicode

Total characters1726
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)5.9%

Sample

1st row420
2nd row350
3rd row600
4th row550
5th row550
ValueCountFrequency (%)
400 46
 
8.0%
500 44
 
7.6%
300 34
 
5.9%
450 30
 
5.2%
600 26
 
4.5%
350 20
 
3.5%
200 17
 
3.0%
430 16
 
2.8%
250 16
 
2.8%
650 15
 
2.6%
Other values (84) 312
54.2%
2024-03-14T10:27:56.358009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 749
43.4%
5 199
 
11.5%
4 184
 
10.7%
3 164
 
9.5%
2 113
 
6.5%
6 104
 
6.0%
8 71
 
4.1%
1 57
 
3.3%
7 55
 
3.2%
9 28
 
1.6%
Other values (2) 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1724
99.9%
Other Punctuation 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 749
43.4%
5 199
 
11.5%
4 184
 
10.7%
3 164
 
9.5%
2 113
 
6.6%
6 104
 
6.0%
8 71
 
4.1%
1 57
 
3.3%
7 55
 
3.2%
9 28
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1726
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 749
43.4%
5 199
 
11.5%
4 184
 
10.7%
3 164
 
9.5%
2 113
 
6.5%
6 104
 
6.0%
8 71
 
4.1%
1 57
 
3.3%
7 55
 
3.2%
9 28
 
1.6%
Other values (2) 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 749
43.4%
5 199
 
11.5%
4 184
 
10.7%
3 164
 
9.5%
2 113
 
6.5%
6 104
 
6.0%
8 71
 
4.1%
1 57
 
3.3%
7 55
 
3.2%
9 28
 
1.6%
Other values (2) 2
 
0.1%

품격명
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
시·군나무
320 
마을나무
256 

Length

Max length5
Median length5
Mean length4.5555556
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
시·군나무 320
55.6%
마을나무 256
44.4%

Length

2024-03-14T10:27:56.468588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:27:56.551642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시·군나무 320
55.6%
마을나무 256
44.4%

지목명
Categorical

Distinct12
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
임야
154 
도로
126 
대
111 
잡종지
78 
전
44 
Other values (7)
63 

Length

Max length4
Median length2
Mean length1.8628472
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
임야 154
26.7%
도로 126
21.9%
대 111
19.3%
잡종지 78
13.5%
전 44
 
7.6%
답 20
 
3.5%
하천 16
 
2.8%
구거 13
 
2.3%
학교용지 6
 
1.0%
유지 4
 
0.7%
Other values (2) 4
 
0.7%

Length

2024-03-14T10:27:56.661941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
임야 154
26.7%
도로 126
21.9%
대 111
19.3%
잡종지 78
13.5%
전 44
 
7.6%
답 20
 
3.5%
하천 16
 
2.8%
구거 13
 
2.3%
학교용지 6
 
1.0%
유지 4
 
0.7%
Other values (2) 4
 
0.7%

지적
Real number (ℝ)

MISSING  SKEWED 

Distinct183
Distinct (%)37.9%
Missing93
Missing (%)16.1%
Infinite0
Infinite (%)0.0%
Mean3671.53
Minimum10
Maximum1290168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2024-03-14T10:27:56.782155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q160
median130
Q3275.5
95-th percentile3482.6
Maximum1290168
Range1290158
Interquartile range (IQR)215.5

Descriptive statistics

Standard deviation58840.45
Coefficient of variation (CV)16.026139
Kurtosis477.0386
Mean3671.53
Median Absolute Deviation (MAD)81
Skewness21.778538
Sum1773349
Variance3.4621986 × 109
MonotonicityNot monotonic
2024-03-14T10:27:56.898706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 28
 
4.9%
60 23
 
4.0%
50 23
 
4.0%
100 23
 
4.0%
150 17
 
3.0%
30 14
 
2.4%
200 14
 
2.4%
300 13
 
2.3%
113 11
 
1.9%
120 10
 
1.7%
Other values (173) 307
53.3%
(Missing) 93
 
16.1%
ValueCountFrequency (%)
10 1
 
0.2%
13 1
 
0.2%
15 1
 
0.2%
16 1
 
0.2%
18 1
 
0.2%
20 7
1.2%
24 1
 
0.2%
25 5
0.9%
26 3
0.5%
29 1
 
0.2%
ValueCountFrequency (%)
1290168 1
0.2%
69692 1
0.2%
42695 1
0.2%
32298 1
0.2%
21207 1
0.2%
20609 1
0.2%
18575 1
0.2%
16099 1
0.2%
14476 1
0.2%
13057 1
0.2%

소유자구분
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
공공
320 
개인
256 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공공 320
55.6%
개인 256
44.4%

Length

2024-03-14T10:27:57.019042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:27:57.158641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공 320
55.6%
개인 256
44.4%

소재지도로명주소
Text

Distinct545
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2024-03-14T10:27:57.455885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length20.675347
Min length15

Characters and Unicode

Total characters11909
Distinct characters258
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

Unique519 ?
Unique (%)90.1%

Sample

1st row전라북도 전주시 완산구 은행로 33-7
2nd row전라북도 전주시 완산구 간납로 91-3
3rd row전라북도 전주시 완산구 향교길 145-22
4th row전라북도 전주시 완산구 향교길 145-22
5th row전라북도 전주시 완산구 향교길 145-22
ValueCountFrequency (%)
전라북도 576
 
20.4%
순창군 74
 
2.6%
남원시 74
 
2.6%
고창군 56
 
2.0%
진안군 55
 
1.9%
무주군 49
 
1.7%
완주군 48
 
1.7%
김제시 44
 
1.6%
정읍시 43
 
1.5%
장수군 37
 
1.3%
Other values (1046) 1772
62.7%
2024-03-14T10:27:57.842729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2256
18.9%
전 618
 
5.2%
북 588
 
4.9%
도 584
 
4.9%
라 576
 
4.8%
면 445
 
3.7%
1 406
 
3.4%
길 394
 
3.3%
군 374
 
3.1%
- 281
 
2.4%
Other values (248) 5387
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7547
63.4%
Space Separator 2256
 
18.9%
Decimal Number 1825
 
15.3%
Dash Punctuation 281
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 618
 
8.2%
북 588
 
7.8%
도 584
 
7.7%
라 576
 
7.6%
면 445
 
5.9%
길 394
 
5.2%
군 374
 
5.0%
시 221
 
2.9%
산 180
 
2.4%
로 162
 
2.1%
Other values (236) 3405
45.1%
Decimal Number
ValueCountFrequency (%)
1 406
22.2%
2 257
14.1%
3 209
11.5%
5 196
10.7%
4 178
9.8%
6 163
8.9%
7 127
 
7.0%
9 104
 
5.7%
8 99
 
5.4%
0 86
 
4.7%
Space Separator
ValueCountFrequency (%)
2256
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 281
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7547
63.4%
Common 4362
36.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 618
 
8.2%
북 588
 
7.8%
도 584
 
7.7%
라 576
 
7.6%
면 445
 
5.9%
길 394
 
5.2%
군 374
 
5.0%
시 221
 
2.9%
산 180
 
2.4%
로 162
 
2.1%
Other values (236) 3405
45.1%
Common
ValueCountFrequency (%)
2256
51.7%
1 406
 
9.3%
- 281
 
6.4%
2 257
 
5.9%
3 209
 
4.8%
5 196
 
4.5%
4 178
 
4.1%
6 163
 
3.7%
7 127
 
2.9%
9 104
 
2.4%
Other values (2) 185
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7547
63.4%
ASCII 4362
36.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2256
51.7%
1 406
 
9.3%
- 281
 
6.4%
2 257
 
5.9%
3 209
 
4.8%
5 196
 
4.5%
4 178
 
4.1%
6 163
 
3.7%
7 127
 
2.9%
9 104
 
2.4%
Other values (2) 185
 
4.2%
Hangul
ValueCountFrequency (%)
전 618
 
8.2%
북 588
 
7.8%
도 584
 
7.7%
라 576
 
7.6%
면 445
 
5.9%
길 394
 
5.2%
군 374
 
5.0%
시 221
 
2.9%
산 180
 
2.4%
로 162
 
2.1%
Other values (236) 3405
45.1%

소재지지번주소
Text

Distinct554
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2024-03-14T10:27:58.141792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length20.710069
Min length14

Characters and Unicode

Total characters11929
Distinct characters213
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique537 ?
Unique (%)93.2%

Sample

1st row전라북도 전주시 완산구 풍남동 3가 36-2
2nd row전라북도 전주시 완산구 풍남동 3가 91-3
3rd row전라북도 전주시 완산구 교동 1가 26-3
4th row전라북도 전주시 완산구 교동 1가 26-3
5th row전라북도 전주시 완산구 교동 1가 26-3
ValueCountFrequency (%)
전라북도 576
 
20.2%
순창군 74
 
2.6%
남원시 74
 
2.6%
고창군 56
 
2.0%
진안군 55
 
1.9%
무주군 49
 
1.7%
완주군 48
 
1.7%
김제시 44
 
1.5%
정읍시 43
 
1.5%
장수군 37
 
1.3%
Other values (1045) 1793
62.9%
2024-03-14T10:27:58.547421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2275
19.1%
전 619
 
5.2%
북 588
 
4.9%
도 586
 
4.9%
라 576
 
4.8%
리 502
 
4.2%
면 444
 
3.7%
1 411
 
3.4%
군 376
 
3.2%
- 253
 
2.1%
Other values (203) 5299
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7431
62.3%
Space Separator 2275
 
19.1%
Decimal Number 1960
 
16.4%
Dash Punctuation 253
 
2.1%
Open Punctuation 5
 
< 0.1%
Close Punctuation 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 619
 
8.3%
북 588
 
7.9%
도 586
 
7.9%
라 576
 
7.8%
리 502
 
6.8%
면 444
 
6.0%
군 376
 
5.1%
시 220
 
3.0%
산 215
 
2.9%
주 159
 
2.1%
Other values (189) 3146
42.3%
Decimal Number
ValueCountFrequency (%)
1 411
21.0%
2 251
12.8%
3 234
11.9%
5 185
9.4%
6 174
8.9%
4 163
 
8.3%
7 143
 
7.3%
0 138
 
7.0%
8 135
 
6.9%
9 126
 
6.4%
Space Separator
ValueCountFrequency (%)
2275
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 253
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7431
62.3%
Common 4498
37.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 619
 
8.3%
북 588
 
7.9%
도 586
 
7.9%
라 576
 
7.8%
리 502
 
6.8%
면 444
 
6.0%
군 376
 
5.1%
시 220
 
3.0%
산 215
 
2.9%
주 159
 
2.1%
Other values (189) 3146
42.3%
Common
ValueCountFrequency (%)
2275
50.6%
1 411
 
9.1%
- 253
 
5.6%
2 251
 
5.6%
3 234
 
5.2%
5 185
 
4.1%
6 174
 
3.9%
4 163
 
3.6%
7 143
 
3.2%
0 138
 
3.1%
Other values (4) 271
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7431
62.3%
ASCII 4498
37.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2275
50.6%
1 411
 
9.1%
- 253
 
5.6%
2 251
 
5.6%
3 234
 
5.2%
5 185
 
4.1%
6 174
 
3.9%
4 163
 
3.6%
7 143
 
3.2%
0 138
 
3.1%
Other values (4) 271
 
6.0%
Hangul
ValueCountFrequency (%)
전 619
 
8.3%
북 588
 
7.9%
도 586
 
7.9%
라 576
 
7.8%
리 502
 
6.8%
면 444
 
6.0%
군 376
 
5.1%
시 220
 
3.0%
산 215
 
2.9%
주 159
 
2.1%
Other values (189) 3146
42.3%

위도
Real number (ℝ)

Distinct543
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.649114
Minimum32.522023
Maximum36.132412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2024-03-14T10:27:58.667112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32.522023
5-th percentile35.35752
Q135.458226
median35.650348
Q335.821268
95-th percentile36.007601
Maximum36.132412
Range3.610389
Interquartile range (IQR)0.36304175

Descriptive statistics

Standard deviation0.28342446
Coefficient of variation (CV)0.007950393
Kurtosis49.693694
Mean35.649114
Median Absolute Deviation (MAD)0.1874035
Skewness-4.5387943
Sum20533.889
Variance0.080329427
MonotonicityNot monotonic
2024-03-14T10:27:58.775604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.813046 5
 
0.9%
35.760824 3
 
0.5%
35.774429 3
 
0.5%
35.974405 3
 
0.5%
35.999917 2
 
0.3%
35.488272 2
 
0.3%
35.643876 2
 
0.3%
35.801138 2
 
0.3%
35.978989 2
 
0.3%
35.33702 2
 
0.3%
Other values (533) 550
95.5%
ValueCountFrequency (%)
32.522023 2
0.3%
35.314891 1
0.2%
35.315562 1
0.2%
35.31577 1
0.2%
35.31587 1
0.2%
35.316845 1
0.2%
35.320613 1
0.2%
35.321666 1
0.2%
35.328503 1
0.2%
35.33001 1
0.2%
ValueCountFrequency (%)
36.132412 1
0.2%
36.095232 1
0.2%
36.085133 1
0.2%
36.083235 1
0.2%
36.083176 1
0.2%
36.082476 1
0.2%
36.074074 1
0.2%
36.060716 1
0.2%
36.059375 1
0.2%
36.058928 1
0.2%

경도
Real number (ℝ)

Distinct545
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.16311
Minimum123.74176
Maximum127.89878
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2024-03-14T10:27:58.909906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum123.74176
5-th percentile126.60946
Q1126.93372
median127.15845
Q3127.39995
95-th percentile127.67292
Maximum127.89878
Range4.157015
Interquartile range (IQR)0.46623075

Descriptive statistics

Standard deviation0.38330044
Coefficient of variation (CV)0.0030142425
Kurtosis20.535837
Mean127.16311
Median Absolute Deviation (MAD)0.2371105
Skewness-2.4906278
Sum73245.95
Variance0.14691923
MonotonicityNot monotonic
2024-03-14T10:27:59.022807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.15763 5
 
0.9%
127.005368 3
 
0.5%
127.21463 3
 
0.5%
127.583652 2
 
0.3%
127.722526 2
 
0.3%
127.012388 2
 
0.3%
127.178484 2
 
0.3%
126.685857 2
 
0.3%
126.879228 2
 
0.3%
127.090617 2
 
0.3%
Other values (535) 551
95.7%
ValueCountFrequency (%)
123.74176 2
0.3%
126.272824 1
0.2%
126.471932 1
0.2%
126.492865 1
0.2%
126.51852 1
0.2%
126.523362 1
0.2%
126.525617 1
0.2%
126.527572 1
0.2%
126.527701 1
0.2%
126.530905 1
0.2%
ValueCountFrequency (%)
127.898775 1
0.2%
127.897601 1
0.2%
127.886722 1
0.2%
127.863005 1
0.2%
127.857285 1
0.2%
127.852648 1
0.2%
127.848696 1
0.2%
127.840462 1
0.2%
127.835791 1
0.2%
127.805396 1
0.2%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2017-11-30
576 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-11-30
2nd row2017-11-30
3rd row2017-11-30
4th row2017-11-30
5th row2017-11-30

Common Values

ValueCountFrequency (%)
2017-11-30 576
100.0%

Length

2024-03-14T10:27:59.116441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:27:59.525507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017-11-30 576
100.0%

Sample

시도명시군구명관리기관명지정번호보호수지정일자보호수해지일자보호수유형명과명학명나무종류그루수나무나이나무높이가슴높이둘레나무갓지름품격명지목명지적소유자구분소재지도로명주소소재지지번주소위도경도데이터기준일자
0전라북도전주시전주시9-11982-09-20<NA>노목은행나무과Ginkgo biloba은행나무158016.0420420마을나무대100개인전라북도 전주시 완산구 은행로 33-7전라북도 전주시 완산구 풍남동 3가 36-235.816985127.152522017-11-30
1전라북도전주시전주시9-1-11982-09-20<NA>노목먹구슬과Cedrela sinensis참죽나무135013.0350350마을나무대132개인전라북도 전주시 완산구 간납로 91-3전라북도 전주시 완산구 풍남동 3가 91-335.818511127.160662017-11-30
2전라북도전주시전주시9-1-31982-09-20<NA>노목은행나무과Ginkgo biloba은행나무138020.0600600마을나무대300개인전라북도 전주시 완산구 향교길 145-22전라북도 전주시 완산구 교동 1가 26-335.813046127.157632017-11-30
3전라북도전주시전주시9-1-41982-09-20<NA>노목은행나무과Ginkgo biloba은행나무135017.0550550마을나무대254개인전라북도 전주시 완산구 향교길 145-22전라북도 전주시 완산구 교동 1가 26-335.813046127.157632017-11-30
4전라북도전주시전주시9-1-51982-09-20<NA>노목은행나무과Ginkgo biloba은행나무135017.0550550마을나무대254개인전라북도 전주시 완산구 향교길 145-22전라북도 전주시 완산구 교동 1가 26-335.813046127.157632017-11-30
5전라북도전주시전주시9-1-61982-09-20<NA>노목느릅나무과Celtis sinensis Persoon팽나무150018.0500500마을나무대176개인전라북도 전주시 덕진구 어은골5길 6전라북도 전주시 덕진구 진북 2가 971-1935.822998127.1312632017-11-30
6전라북도전주시전주시9-1-71982-09-202010-08-24노목느티나무과Zelkova serrata느티나무040016.0470470마을나무대113개인전라북도 전주시 완산구 완산2길 15-3전라북도 전주시 완산구 서완산동 1가 25-135.810679127.1403052017-11-30
7전라북도전주시전주시9-1-81982-09-20<NA>노목느티나무과Zelkova serrata느티나무134012.0400400시·군나무임야63공공전라북도 전주시 완산구 서원로 386전라북도 전주시 완산구 중화산동 1가 150-335.813597127.1361242017-11-30
8전라북도전주시전주시9-1-91982-09-20<NA>노목버드나무과Salix koreensis ANDERSS.버드나무140014.0450450시·군나무임야78공공전라북도 전주시 덕진구 사평1길 9-3전라북도 전주시 덕진구 덕진동 1가 141435.841349127.1201382017-11-30
9전라북도전주시전주시9-1-111982-09-20<NA>노목버드나무과Salix koreensis ANDERSS.버드나무138016.0520520시·군나무도로176공공전라북도 전주시 완산구 평화로 157전라북도 전주시 완산구 평화동 3가 86835.790682127.135622017-11-30
시도명시군구명관리기관명지정번호보호수지정일자보호수해지일자보호수유형명과명학명나무종류그루수나무나이나무높이가슴높이둘레나무갓지름품격명지목명지적소유자구분소재지도로명주소소재지지번주소위도경도데이터기준일자
566전라북도부안군부안군9-15-6-11982-09-20<NA>노목느릅나무과Celtis sinensis Persoon팽나무125015.0550550마을나무전250개인전라북도 부안군 보안면 우동길 55전라북도 부안군 보안면 우동리 438-335.620178126.6396752017-11-30
567전라북도부안군부안군9-15-7-11982-09-20<NA>노목느티나무과Zelkova serrata느티나무124021.0550550마을나무대250개인전라북도 부안군 진서면 석포용동길 70-21전라북도 부안군 진서면 석포리 6435.599522126.5912052017-11-30
568전라북도부안군부안군9-15-11-11982-09-20<NA>노목느릅나무과Celtis sinensis Persoon팽나무125016.0760760마을나무전112개인전라북도 부안군 하서면 금광길 63-3전라북도 부안군 하서면 백련리 232-335.705222126.6135582017-11-30
569전라북도부안군부안군9-15-6-3-11982-09-20<NA>노목느릅나무과Celtis sinensis Persoon팽나무115016.0420420시·군나무도로277공공전라북도 부안군 보안면 청자로 1365-9전라북도 부안군 보안면 우동리 14135.610878126.6389722017-11-30
570전라북도부안군부안군9-15-7-6-11982-09-20<NA>노목느릅나무과Celtis sinensis Persoon팽나무11506.0660660마을나무전184개인전라북도 부안군 변산면 새만금로 6전라북도 부안군 변산면 대항리 14335.697164126.5590282017-11-30
571전라북도부안군부안군9-15-10-1-11982-09-20<NA>노목느티나무과Zelkova serrata느티나무115019.0440440시·군나무도로157공공전라북도 부안군 상서면 청등길 42전라북도 부안군 상서면 감교리 82935.667458126.6775522017-11-30
572전라북도부안군부안군9-15-10-1-21982-09-20<NA>노목느티나무과Zelkova serrata느티나무115021.0540540시·군나무도로279공공전라북도 부안군 상서면 청등길 23-5전라북도 부안군 상서면 감교리 82835.668363126.6763122017-11-30
573전라북도부안군부안군9-15-51998-10-07<NA>노목부처꽃과Lagerstroemia indica배롱나무33706.0200200마을나무대843개인전라북도 부안군 하서면 석불산길 175전라북도 부안군 하서면 청호리 80835.728562126.6385752017-11-30
574전라북도부안군부안군9-15-61998-10-07<NA>노목소나무과Pinus densiflora소나무134012.0220220마을나무대843개인전라북도 부안군 하서면 석불산길 175전라북도 부안군 하서면 청호리 80835.728562126.6385752017-11-30
575전라북도부안군부안군9-15-72013-10-07<NA>희귀목부처꽃과Lagerstroemia indica배롱나무13008.0180180마을나무대294개인전라북도 부안군 위도면 내원암길 42전라북도 부안군 위도면 치도리 36235.588115126.2728242017-11-30