Overview

Dataset statistics

Number of variables11
Number of observations90
Missing cells31
Missing cells (%)3.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.2 KiB
Average record size in memory93.5 B

Variable types

Text2
Numeric4
Categorical3
DateTime2

Dataset

Description충청남도 보령시의 무인도서 현황 데이터를 제공합니다. 도서명, 주소, 위도, 경도, 토지 전체 면적, 육지와의 거리, 지목, 무인도서 관리유형, 주변해역 관리유형, 지정고시일(법령고시일), 데이터 기준일로 구성되어있습니다.
URLhttps://www.data.go.kr/data/15104119/fileData.do

Alerts

데이터기준일 has constant value ""Constant
무인도서 관리유형 is highly overall correlated with 주변해역 관리유형High correlation
주변해역 관리유형 is highly overall correlated with 무인도서 관리유형High correlation
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
토지 전체 면적 is highly overall correlated with 지목High correlation
육지와의 거리 is highly overall correlated with 지목High correlation
지목 is highly overall correlated with 토지 전체 면적 and 1 other fieldsHigh correlation
지목 is highly imbalanced (51.4%)Imbalance
지정고시일(법령고시일) has 31 (34.4%) missing valuesMissing
토지 전체 면적 has 23 (25.6%) zerosZeros
육지와의 거리 has 3 (3.3%) zerosZeros

Reproduction

Analysis started2023-12-12 05:00:09.237427
Analysis finished2023-12-12 05:00:12.384385
Duration3.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

무인도서명
Text

Distinct70
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Memory size852.0 B
2023-12-12T14:00:12.619214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length4.0444444
Min length2

Characters and Unicode

Total characters364
Distinct characters102
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

Unique65 ?
Unique (%)72.2%

Sample

1st row길산서
2nd row왁새섬
3rd row흑도
4th row흑서
5th row(미부여)
ValueCountFrequency (%)
미부여 17
 
18.5%
오도 2
 
2.2%
황도 2
 
2.2%
흑도 2
 
2.2%
석도 2
 
2.2%
안마도 1
 
1.1%
소군관도(작은궁과무니 1
 
1.1%
소신이 1
 
1.1%
삼형제도 1
 
1.1%
모도 1
 
1.1%
Other values (62) 62
67.4%
2023-12-12T14:00:13.129253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 58
 
15.9%
( 25
 
6.9%
) 25
 
6.9%
여 21
 
5.8%
부 17
 
4.7%
미 17
 
4.7%
섬 9
 
2.5%
대 8
 
2.2%
산 8
 
2.2%
명 7
 
1.9%
Other values (92) 169
46.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 305
83.8%
Open Punctuation 25
 
6.9%
Close Punctuation 25
 
6.9%
Decimal Number 5
 
1.4%
Other Punctuation 2
 
0.5%
Space Separator 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 58
 
19.0%
여 21
 
6.9%
부 17
 
5.6%
미 17
 
5.6%
섬 9
 
3.0%
대 8
 
2.6%
산 8
 
2.6%
명 7
 
2.3%
무 7
 
2.3%
서 7
 
2.3%
Other values (83) 146
47.9%
Decimal Number
ValueCountFrequency (%)
1 1
20.0%
2 1
20.0%
3 1
20.0%
5 1
20.0%
4 1
20.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 305
83.8%
Common 59
 
16.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 58
 
19.0%
여 21
 
6.9%
부 17
 
5.6%
미 17
 
5.6%
섬 9
 
3.0%
대 8
 
2.6%
산 8
 
2.6%
명 7
 
2.3%
무 7
 
2.3%
서 7
 
2.3%
Other values (83) 146
47.9%
Common
ValueCountFrequency (%)
( 25
42.4%
) 25
42.4%
, 2
 
3.4%
2
 
3.4%
1 1
 
1.7%
2 1
 
1.7%
3 1
 
1.7%
5 1
 
1.7%
4 1
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 305
83.8%
ASCII 59
 
16.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 58
 
19.0%
여 21
 
6.9%
부 17
 
5.6%
미 17
 
5.6%
섬 9
 
3.0%
대 8
 
2.6%
산 8
 
2.6%
명 7
 
2.3%
무 7
 
2.3%
서 7
 
2.3%
Other values (83) 146
47.9%
ASCII
ValueCountFrequency (%)
( 25
42.4%
) 25
42.4%
, 2
 
3.4%
2
 
3.4%
1 1
 
1.7%
2 1
 
1.7%
3 1
 
1.7%
5 1
 
1.7%
4 1
 
1.7%

주소
Text

Distinct77
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Memory size852.0 B
2023-12-12T14:00:13.433346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length28
Mean length21.522222
Min length17

Characters and Unicode

Total characters1937
Distinct characters45
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

Unique68 ?
Unique (%)75.6%

Sample

1st row충청남도 보령시 오천면 녹도리
2nd row충청남도 보령시 오천면 효자도리
3rd row충청남도 보령시 오천면 외연도리
4th row충청남도 보령시 오천면 녹도리
5th row충청남도 보령시 주교면 송학리 1026
ValueCountFrequency (%)
충청남도 90
19.4%
보령시 90
19.4%
오천면 82
17.7%
외연도리 36
 
7.8%
녹도리 20
 
4.3%
효자도리 14
 
3.0%
외 9
 
1.9%
삽시도리 9
 
1.9%
웅천읍 5
 
1.1%
독산리 4
 
0.9%
Other values (78) 104
22.5%
2023-12-12T14:00:13.949236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
378
19.5%
도 172
 
8.9%
시 99
 
5.1%
충 90
 
4.6%
청 90
 
4.6%
남 90
 
4.6%
보 90
 
4.6%
령 90
 
4.6%
리 89
 
4.6%
천 87
 
4.5%
Other values (35) 662
34.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1315
67.9%
Space Separator 378
 
19.5%
Decimal Number 240
 
12.4%
Dash Punctuation 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 172
13.1%
시 99
 
7.5%
충 90
 
6.8%
청 90
 
6.8%
남 90
 
6.8%
보 90
 
6.8%
령 90
 
6.8%
리 89
 
6.8%
천 87
 
6.6%
면 84
 
6.4%
Other values (23) 334
25.4%
Decimal Number
ValueCountFrequency (%)
1 43
17.9%
4 43
17.9%
2 35
14.6%
5 25
10.4%
0 21
8.8%
8 20
8.3%
3 19
7.9%
7 14
 
5.8%
6 13
 
5.4%
9 7
 
2.9%
Space Separator
ValueCountFrequency (%)
378
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1315
67.9%
Common 622
32.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 172
13.1%
시 99
 
7.5%
충 90
 
6.8%
청 90
 
6.8%
남 90
 
6.8%
보 90
 
6.8%
령 90
 
6.8%
리 89
 
6.8%
천 87
 
6.6%
면 84
 
6.4%
Other values (23) 334
25.4%
Common
ValueCountFrequency (%)
378
60.8%
1 43
 
6.9%
4 43
 
6.9%
2 35
 
5.6%
5 25
 
4.0%
0 21
 
3.4%
8 20
 
3.2%
3 19
 
3.1%
7 14
 
2.3%
6 13
 
2.1%
Other values (2) 11
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1315
67.9%
ASCII 622
32.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
378
60.8%
1 43
 
6.9%
4 43
 
6.9%
2 35
 
5.6%
5 25
 
4.0%
0 21
 
3.4%
8 20
 
3.2%
3 19
 
3.1%
7 14
 
2.3%
6 13
 
2.1%
Other values (2) 11
 
1.8%
Hangul
ValueCountFrequency (%)
도 172
13.1%
시 99
 
7.5%
충 90
 
6.8%
청 90
 
6.8%
남 90
 
6.8%
보 90
 
6.8%
령 90
 
6.8%
리 89
 
6.8%
천 87
 
6.6%
면 84
 
6.4%
Other values (23) 334
25.4%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.254974
Minimum36.12387
Maximum36.419357
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-12T14:00:14.126797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.12387
5-th percentile36.129788
Q136.202564
median36.237104
Q336.308491
95-th percentile36.413013
Maximum36.419357
Range0.29548742
Interquartile range (IQR)0.10592619

Descriptive statistics

Standard deviation0.090117199
Coefficient of variation (CV)0.0024856506
Kurtosis-0.81542685
Mean36.254974
Median Absolute Deviation (MAD)0.06425328
Skewness0.43142075
Sum3262.9477
Variance0.0081211096
MonotonicityNot monotonic
2023-12-12T14:00:14.287106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.4029629 3
 
3.3%
36.25936979 3
 
3.3%
36.22176829 3
 
3.3%
36.29931597 3
 
3.3%
36.23710428 2
 
2.2%
36.31466106 2
 
2.2%
36.24002448 2
 
2.2%
36.165783 2
 
2.2%
36.165969 1
 
1.1%
36.29322163 1
 
1.1%
Other values (68) 68
75.6%
ValueCountFrequency (%)
36.12387 1
1.1%
36.124462 1
1.1%
36.125042 1
1.1%
36.125641 1
1.1%
36.12871 1
1.1%
36.131106 1
1.1%
36.132162 1
1.1%
36.13389 1
1.1%
36.135828 1
1.1%
36.135944 1
1.1%
ValueCountFrequency (%)
36.41935742 1
 
1.1%
36.41718698 1
 
1.1%
36.41680122 1
 
1.1%
36.41540739 1
 
1.1%
36.41305889 1
 
1.1%
36.41295708 1
 
1.1%
36.41178414 1
 
1.1%
36.40486626 1
 
1.1%
36.40400379 1
 
1.1%
36.4029629 3
3.3%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.18113
Minimum125.57215
Maximum126.53093
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-12T14:00:14.433959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125.57215
5-th percentile125.59483
Q1126.06377
median126.24205
Q3126.33285
95-th percentile126.46721
Maximum126.53093
Range0.9587832
Interquartile range (IQR)0.26908142

Descriptive statistics

Standard deviation0.23346388
Coefficient of variation (CV)0.0018502281
Kurtosis1.0875021
Mean126.18113
Median Absolute Deviation (MAD)0.1435463
Skewness-1.0533111
Sum11356.302
Variance0.054505385
MonotonicityNot monotonic
2023-12-12T14:00:14.586486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.1045147 3
 
3.3%
126.4634513 3
 
3.3%
126.2901706 3
 
3.3%
126.3195565 3
 
3.3%
126.122115 2
 
2.2%
125.9572665 2
 
2.2%
125.9570026 2
 
2.2%
126.124452 2
 
2.2%
125.595932 2
 
2.2%
126.3486686 2
 
2.2%
Other values (66) 66
73.3%
ValueCountFrequency (%)
125.572147 1
1.1%
125.572342 1
1.1%
125.572482 1
1.1%
125.575337 1
1.1%
125.593928 1
1.1%
125.595932 2
2.2%
125.9570026 2
2.2%
125.9572665 2
2.2%
125.9574555 1
1.1%
126.0052 1
1.1%
ValueCountFrequency (%)
126.5309302 1
 
1.1%
126.5083338 1
 
1.1%
126.4722667 1
 
1.1%
126.4681431 1
 
1.1%
126.4679105 1
 
1.1%
126.4663609 1
 
1.1%
126.4655403 1
 
1.1%
126.4634513 3
3.3%
126.4632941 1
 
1.1%
126.4612346 1
 
1.1%

토지 전체 면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)71.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28948.144
Minimum0
Maximum558894
Zeros23
Zeros (%)25.6%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-12T14:00:14.739493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q161.25
median3885.5
Q313658.25
95-th percentile140643
Maximum558894
Range558894
Interquartile range (IQR)13597

Descriptive statistics

Standard deviation91487.788
Coefficient of variation (CV)3.1604025
Kurtosis22.852997
Mean28948.144
Median Absolute Deviation (MAD)3885.5
Skewness4.6947733
Sum2605333
Variance8.3700153 × 109
MonotonicityNot monotonic
2023-12-12T14:00:14.929421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
25.6%
15273 2
 
2.2%
16944 2
 
2.2%
3471 2
 
2.2%
27769 2
 
2.2%
4300 1
 
1.1%
25388 1
 
1.1%
11306 1
 
1.1%
24298 1
 
1.1%
16463 1
 
1.1%
Other values (54) 54
60.0%
ValueCountFrequency (%)
0 23
25.6%
245 1
 
1.1%
403 1
 
1.1%
881 1
 
1.1%
1009 1
 
1.1%
1100 1
 
1.1%
1190 1
 
1.1%
1314 1
 
1.1%
1388 1
 
1.1%
1587 1
 
1.1%
ValueCountFrequency (%)
558894 1
1.1%
527818 1
1.1%
301690 1
1.1%
286017 1
1.1%
180783 1
1.1%
91583 1
1.1%
60595 1
1.1%
53455 1
1.1%
35504 1
1.1%
27769 2
2.2%

육지와의 거리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)67.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.593556
Minimum0
Maximum61
Zeros3
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-12T14:00:15.072545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.154
Q13
median9.55
Q330.4625
95-th percentile42.615
Maximum61
Range61
Interquartile range (IQR)27.4625

Descriptive statistics

Standard deviation16.122009
Coefficient of variation (CV)1.0338892
Kurtosis-0.59446416
Mean15.593556
Median Absolute Deviation (MAD)8.41
Skewness0.85050975
Sum1403.42
Variance259.91918
MonotonicityNot monotonic
2023-12-12T14:00:15.244671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.0 19
 
21.1%
0.0 3
 
3.3%
10.1 3
 
3.3%
27.5 2
 
2.2%
9.0 2
 
2.2%
1.0 2
 
2.2%
21.0 2
 
2.2%
10.2 2
 
2.2%
40.34 2
 
2.2%
0.7 2
 
2.2%
Other values (51) 51
56.7%
ValueCountFrequency (%)
0.0 3
3.3%
0.01 1
 
1.1%
0.1 1
 
1.1%
0.22 1
 
1.1%
0.23 1
 
1.1%
0.25 1
 
1.1%
0.3 1
 
1.1%
0.7 2
2.2%
0.8 1
 
1.1%
1.0 2
2.2%
ValueCountFrequency (%)
61.0 1
1.1%
50.0 1
1.1%
47.0 1
1.1%
43.5 1
1.1%
43.2 1
1.1%
41.9 1
1.1%
41.5 1
1.1%
41.1 1
1.1%
41.0 1
1.1%
40.63 1
1.1%

지목
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size852.0 B
임야
63 
<NA>
23 
임야+전+대
 
2
임야+전+대+도로+묘지
 
1
전+대+임야
 
1

Length

Max length12
Median length2
Mean length2.7555556
Min length2

Unique

Unique2 ?
Unique (%)2.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
임야 63
70.0%
<NA> 23
 
25.6%
임야+전+대 2
 
2.2%
임야+전+대+도로+묘지 1
 
1.1%
전+대+임야 1
 
1.1%

Length

2023-12-12T14:00:15.409142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:00:15.543332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임야 63
70.0%
na 23
 
25.6%
임야+전+대 2
 
2.2%
임야+전+대+도로+묘지 1
 
1.1%
전+대+임야 1
 
1.1%

무인도서 관리유형
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size852.0 B
이용가능
37 
미지정
35 
준보전
12 
개발가능
6 

Length

Max length4
Median length3
Mean length3.4777778
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row이용가능
2nd row이용가능
3rd row이용가능
4th row이용가능
5th row미지정

Common Values

ValueCountFrequency (%)
이용가능 37
41.1%
미지정 35
38.9%
준보전 12
 
13.3%
개발가능 6
 
6.7%

Length

2023-12-12T14:00:15.695107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:00:15.849862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
이용가능 37
41.1%
미지정 35
38.9%
준보전 12
 
13.3%
개발가능 6
 
6.7%

주변해역 관리유형
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size852.0 B
이용가능
37 
미지정
31 
준보전
15 
개발가능
6 
절대보전
 
1

Length

Max length4
Median length3
Mean length3.4888889
Min length3

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row이용가능
2nd row이용가능
3rd row이용가능
4th row이용가능
5th row미지정

Common Values

ValueCountFrequency (%)
이용가능 37
41.1%
미지정 31
34.4%
준보전 15
16.7%
개발가능 6
 
6.7%
절대보전 1
 
1.1%

Length

2023-12-12T14:00:16.000273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:00:16.150804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
이용가능 37
41.1%
미지정 31
34.4%
준보전 15
16.7%
개발가능 6
 
6.7%
절대보전 1
 
1.1%

지정고시일(법령고시일)
Date

MISSING 

Distinct4
Distinct (%)6.8%
Missing31
Missing (%)34.4%
Memory size852.0 B
Minimum2010-10-08 00:00:00
Maximum2015-12-31 00:00:00
2023-12-12T14:00:16.281179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:16.378695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size852.0 B
Minimum2023-08-28 00:00:00
Maximum2023-08-28 00:00:00
2023-12-12T14:00:16.479605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:16.575719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T14:00:11.570098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:10.003400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:10.392864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:11.109829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:11.690814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:10.095221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:10.807873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:11.210445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:11.790893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:10.177550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:10.905714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:11.318509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:11.911911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:10.264587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:11.012942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:11.430670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:00:16.671191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
무인도서명주소위도경도토지 전체 면적육지와의 거리지목무인도서 관리유형주변해역 관리유형지정고시일(법령고시일)
무인도서명1.0000.9880.8260.0000.9180.6640.0000.9860.8390.445
주소0.9881.0001.0001.0001.0000.9001.0000.8730.9431.000
위도0.8261.0001.0000.7610.0000.7210.0000.3370.7460.257
경도0.0001.0000.7611.0000.3140.6350.0500.0400.0650.486
토지 전체 면적0.9181.0000.0000.3141.0000.7600.7370.3360.5350.000
육지와의 거리0.6640.9000.7210.6350.7601.0000.7680.6930.7790.704
지목0.0001.0000.0000.0500.7370.7681.0000.5430.2770.000
무인도서 관리유형0.9860.8730.3370.0400.3360.6930.5431.0000.9340.804
주변해역 관리유형0.8390.9430.7460.0650.5350.7790.2770.9341.0000.674
지정고시일(법령고시일)0.4451.0000.2570.4860.0000.7040.0000.8040.6741.000
2023-12-12T14:00:16.806302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
무인도서 관리유형주변해역 관리유형지목
무인도서 관리유형1.0000.9460.235
주변해역 관리유형0.9461.0000.225
지목0.2350.2251.000
2023-12-12T14:00:16.909831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도토지 전체 면적육지와의 거리지목무인도서 관리유형주변해역 관리유형
위도1.0000.702-0.166-0.3440.0000.1970.390
경도0.7021.000-0.135-0.4570.0000.0000.029
토지 전체 면적-0.166-0.1351.000-0.0120.6740.2780.223
육지와의 거리-0.344-0.457-0.0121.0000.5520.4760.421
지목0.0000.0000.6740.5521.0000.2350.225
무인도서 관리유형0.1970.0000.2780.4760.2351.0000.946
주변해역 관리유형0.3900.0290.2230.4210.2250.9461.000

Missing values

2023-12-12T14:00:12.071821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:00:12.281043image/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

무인도서명주소위도경도토지 전체 면적육지와의 거리지목무인도서 관리유형주변해역 관리유형지정고시일(법령고시일)데이터기준일
0길산서충청남도 보령시 오천면 녹도리36.165969126.122449027.5<NA>이용가능이용가능2015-12-312023-08-28
1왁새섬충청남도 보령시 오천면 효자도리36.241674126.2867603.0<NA>이용가능이용가능2010-10-082023-08-28
2흑도충청남도 보령시 오천면 외연도리36.135944126.35526041.1<NA>이용가능이용가능2015-12-312023-08-28
3흑서충청남도 보령시 오천면 녹도리36.174624126.143888023.7<NA>이용가능이용가능2015-12-312023-08-28
4(미부여)충청남도 보령시 주교면 송학리 102636.225347126.28454500.23<NA>미지정미지정<NA>2023-08-28
5딴명장섬충청남도 보령시 오천면 삽시도리 121336.404004126.33446982302.73임야미지정미지정<NA>2023-08-28
6(미부여)충청남도 보령시 오천면 삽시도리 127936.402584126.33536102.84<NA>미지정미지정<NA>2023-08-28
7(미부여)충청남도 보령시 오천면 삽시도리 128036.314661126.348669010.79<NA>미지정미지정<NA>2023-08-28
8(미부여)충청남도 보령시 오천면 삽시도리 128036.314661126.348669293810.9임야미지정미지정<NA>2023-08-28
9소청도충청남도 보령시 오천면 외연도리 47336.236794126.077283215441.9임야이용가능이용가능2015-12-312023-08-28
무인도서명주소위도경도토지 전체 면적육지와의 거리지목무인도서 관리유형주변해역 관리유형지정고시일(법령고시일)데이터기준일
80질마도충청남도 보령시 오천면 외연도리 산53936.210418126.075446190540.0임야준보전준보전2014-07-152023-08-28
81관장도충청남도 보령시 오천면 외연도리 산54036.253321126.10431169303.0임야이용가능이용가능2010-10-082023-08-28
82변도충청남도 보령시 오천면 외연도리 산54136.153107125.593928243660.0임야미지정준보전2015-12-312023-08-28
83외횡견도충청남도 보령시 오천면 외연도리 산54236.210958126.0412162230743.5임야미지정준보전2015-12-312023-08-28
84석도충청남도 보령시 오천면 외연도리 산54336.209261126.05811466850.0임야미지정준보전2015-12-312023-08-28
85황도충청남도 보령시 오천면 외연도리 산54436.223095126.05452710090.0임야이용가능이용가능2015-12-312023-08-28
86설몽도충청남도 보령시 오천면 외연도리 산545 외 2필지36.125042126.005266983.0임야이용가능이용가능2010-10-082023-08-28
87죽도(대섬)충청남도 보령시 주교면 송학리 산8536.36999126.508334119011.0임야이용가능이용가능2010-10-082023-08-28
88(미부여)충청남도 보령시 오천면 녹도리 산936.293354126.260038017.64<NA>미지정미지정<NA>2023-08-28
89아랫노랑이섬충청남도 보령시 오천면 효자도리 산936.411784126.46791158510.25임야준보전준보전2014-07-152023-08-28