Overview

Dataset statistics

Number of variables8
Number of observations536
Missing cells8
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.2 KiB
Average record size in memory69.2 B

Variable types

Numeric5
Text2
Categorical1

Dataset

Description국립공원 구역 내에 존재하는 530여개 유무인도서에 관한 도서명, 지구명, 위치, 면적, 해안선 길이(km) 등 정보를 CSV 포맷으로 제공합니다.
Author국립공원공단
URLhttps://www.data.go.kr/data/15003423/fileData.do

Alerts

번호 is highly overall correlated with 지구명High correlation
인구수 is highly overall correlated with 가구수 and 1 other fieldsHigh correlation
가구수 is highly overall correlated with 인구수 and 1 other fieldsHigh correlation
해안선길이 is highly overall correlated with 인구수 and 1 other fieldsHigh correlation
지구명 is highly overall correlated with 번호High correlation
해안선길이 is highly skewed (γ1 = 23.1393762)Skewed
번호 has unique valuesUnique
면적 has 18 (3.4%) zerosZeros
인구수 has 435 (81.2%) zerosZeros
가구수 has 437 (81.5%) zerosZeros
해안선길이 has 345 (64.4%) zerosZeros

Reproduction

Analysis started2023-12-12 07:36:33.267187
Analysis finished2023-12-12 07:36:37.035230
Duration3.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct536
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.5
Minimum1
Maximum536
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-12-12T16:36:37.115161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile27.75
Q1134.75
median268.5
Q3402.25
95-th percentile509.25
Maximum536
Range535
Interquartile range (IQR)267.5

Descriptive statistics

Standard deviation154.87414
Coefficient of variation (CV)0.57681245
Kurtosis-1.2
Mean268.5
Median Absolute Deviation (MAD)134
Skewness0
Sum143916
Variance23986
MonotonicityNot monotonic
2023-12-12T16:36:37.289773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 1
 
0.2%
340 1
 
0.2%
232 1
 
0.2%
231 1
 
0.2%
230 1
 
0.2%
208 1
 
0.2%
288 1
 
0.2%
287 1
 
0.2%
286 1
 
0.2%
285 1
 
0.2%
Other values (526) 526
98.1%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
536 1
0.2%
535 1
0.2%
534 1
0.2%
533 1
0.2%
532 1
0.2%
531 1
0.2%
530 1
0.2%
529 1
0.2%
528 1
0.2%
527 1
0.2%

도서명
Text

Distinct483
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
2023-12-12T16:36:37.566932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length3.4347015
Min length2

Characters and Unicode

Total characters1841
Distinct characters264
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

Unique447 ?
Unique (%)83.4%

Sample

1st row구도
2nd row소안도
3rd row당사도
4th row소분점도 2
5th row화기서
ValueCountFrequency (%)
2 10
 
1.7%
여 7
 
1.2%
형제도 7
 
1.2%
1 6
 
1.0%
송도 5
 
0.9%
솔섬 5
 
0.9%
추도 4
 
0.7%
옆 4
 
0.7%
장구섬 4
 
0.7%
죽도 4
 
0.7%
Other values (468) 521
90.3%
2023-12-12T16:36:37.919938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 389
 
21.1%
섬 105
 
5.7%
여 54
 
2.9%
소 47
 
2.6%
대 47
 
2.6%
41
 
2.2%
( 37
 
2.0%
) 37
 
2.0%
2 25
 
1.4%
목 23
 
1.2%
Other values (254) 1036
56.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1661
90.2%
Decimal Number 65
 
3.5%
Space Separator 41
 
2.2%
Open Punctuation 37
 
2.0%
Close Punctuation 37
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 389
23.4%
섬 105
 
6.3%
여 54
 
3.3%
소 47
 
2.8%
대 47
 
2.8%
목 23
 
1.4%
장 22
 
1.3%
바 21
 
1.3%
항 20
 
1.2%
위 18
 
1.1%
Other values (244) 915
55.1%
Decimal Number
ValueCountFrequency (%)
2 25
38.5%
1 20
30.8%
3 8
 
12.3%
4 5
 
7.7%
5 4
 
6.2%
6 2
 
3.1%
9 1
 
1.5%
Space Separator
ValueCountFrequency (%)
41
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1661
90.2%
Common 180
 
9.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 389
23.4%
섬 105
 
6.3%
여 54
 
3.3%
소 47
 
2.8%
대 47
 
2.8%
목 23
 
1.4%
장 22
 
1.3%
바 21
 
1.3%
항 20
 
1.2%
위 18
 
1.1%
Other values (244) 915
55.1%
Common
ValueCountFrequency (%)
41
22.8%
( 37
20.6%
) 37
20.6%
2 25
13.9%
1 20
11.1%
3 8
 
4.4%
4 5
 
2.8%
5 4
 
2.2%
6 2
 
1.1%
9 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1661
90.2%
ASCII 180
 
9.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 389
23.4%
섬 105
 
6.3%
여 54
 
3.3%
소 47
 
2.8%
대 47
 
2.8%
목 23
 
1.4%
장 22
 
1.3%
바 21
 
1.3%
항 20
 
1.2%
위 18
 
1.1%
Other values (244) 915
55.1%
ASCII
ValueCountFrequency (%)
41
22.8%
( 37
20.6%
) 37
20.6%
2 25
13.9%
1 20
11.1%
3 8
 
4.4%
4 5
 
2.8%
5 4
 
2.2%
6 2
 
1.1%
9 1
 
0.6%

지구명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
조도지구
103 
비금도초지구
63 
소안청산지구
54 
통영지구
43 
나로도지구
41 
Other values (12)
232 

Length

Max length6
Median length5
Mean length4.9011194
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row소안청산지구
2nd row소안청산지구
3rd row소안청산지구
4th row원북지구
5th row소근지구

Common Values

ValueCountFrequency (%)
조도지구 103
19.2%
비금도초지구 63
11.8%
소안청산지구 54
10.1%
통영지구 43
8.0%
나로도지구 41
 
7.6%
흑산홍도지구 38
 
7.1%
금오도지구 35
 
6.5%
소근지구 34
 
6.3%
거제지구 28
 
5.2%
고흥백도지구 26
 
4.9%
Other values (7) 71
13.2%

Length

2023-12-12T16:36:38.045208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
조도지구 103
19.2%
비금도초지구 63
11.8%
소안청산지구 54
10.1%
통영지구 43
8.0%
나로도지구 41
 
7.6%
흑산홍도지구 38
 
7.1%
금오도지구 35
 
6.5%
소근지구 34
 
6.3%
거제지구 28
 
5.2%
고흥백도지구 26
 
4.9%
Other values (7) 71
13.2%

위치
Text

Distinct484
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
2023-12-12T16:36:38.294627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length30
Mean length20.854478
Min length15

Characters and Unicode

Total characters11178
Distinct characters176
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique454 ?
Unique (%)84.7%

Sample

1st row전라남도 완도군 소안면 횡간리 187
2nd row전라남도 완도군 소안면 비자리 193
3rd row전라남도 완도군 소안면 당사리 산 91-12
4th row충청남도 태안군 원북면 방갈리
5th row충청남도 태안군 근흥면 신진도리 20
ValueCountFrequency (%)
전라남도 360
 
13.2%
산 111
 
4.1%
진도군 103
 
3.8%
신안군 101
 
3.7%
경상남도 101
 
3.7%
조도면 97
 
3.6%
충청남도 74
 
2.7%
태안군 64
 
2.4%
여수시 60
 
2.2%
완도군 54
 
2.0%
Other values (573) 1595
58.6%
2023-12-12T16:36:38.651538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2184
19.5%
도 1054
 
9.4%
남 629
 
5.6%
리 518
 
4.6%
면 492
 
4.4%
산 459
 
4.1%
군 378
 
3.4%
라 366
 
3.3%
전 361
 
3.2%
1 286
 
2.6%
Other values (166) 4451
39.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7514
67.2%
Space Separator 2184
 
19.5%
Decimal Number 1320
 
11.8%
Dash Punctuation 79
 
0.7%
Open Punctuation 33
 
0.3%
Close Punctuation 33
 
0.3%
Other Punctuation 13
 
0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 1054
 
14.0%
남 629
 
8.4%
리 518
 
6.9%
면 492
 
6.5%
산 459
 
6.1%
군 378
 
5.0%
라 366
 
4.9%
전 361
 
4.8%
안 196
 
2.6%
시 166
 
2.2%
Other values (150) 2895
38.5%
Decimal Number
ValueCountFrequency (%)
1 286
21.7%
2 185
14.0%
4 150
11.4%
6 123
9.3%
3 115
8.7%
5 105
 
8.0%
7 98
 
7.4%
9 96
 
7.3%
0 84
 
6.4%
8 78
 
5.9%
Space Separator
ValueCountFrequency (%)
2184
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 79
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7514
67.2%
Common 3664
32.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 1054
 
14.0%
남 629
 
8.4%
리 518
 
6.9%
면 492
 
6.5%
산 459
 
6.1%
군 378
 
5.0%
라 366
 
4.9%
전 361
 
4.8%
안 196
 
2.6%
시 166
 
2.2%
Other values (150) 2895
38.5%
Common
ValueCountFrequency (%)
2184
59.6%
1 286
 
7.8%
2 185
 
5.0%
4 150
 
4.1%
6 123
 
3.4%
3 115
 
3.1%
5 105
 
2.9%
7 98
 
2.7%
9 96
 
2.6%
0 84
 
2.3%
Other values (6) 238
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7514
67.2%
ASCII 3664
32.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2184
59.6%
1 286
 
7.8%
2 185
 
5.0%
4 150
 
4.1%
6 123
 
3.4%
3 115
 
3.1%
5 105
 
2.9%
7 98
 
2.7%
9 96
 
2.6%
0 84
 
2.3%
Other values (6) 238
 
6.5%
Hangul
ValueCountFrequency (%)
도 1054
 
14.0%
남 629
 
8.4%
리 518
 
6.9%
면 492
 
6.5%
산 459
 
6.1%
군 378
 
5.0%
라 366
 
4.9%
전 361
 
4.8%
안 196
 
2.6%
시 166
 
2.2%
Other values (150) 2895
38.5%

면적
Real number (ℝ)

ZEROS 

Distinct446
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean744416.98
Minimum0
Maximum45250000
Zeros18
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-12-12T16:36:38.770797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.75
Q1959.75
median6614.5
Q354118.5
95-th percentile1612500
Maximum45250000
Range45250000
Interquartile range (IQR)53158.75

Descriptive statistics

Standard deviation4176072.2
Coefficient of variation (CV)5.6098562
Kurtosis69.119543
Mean744416.98
Median Absolute Deviation (MAD)6597.5
Skewness7.9861937
Sum3.990075 × 108
Variance1.7439579 × 1013
MonotonicityNot monotonic
2023-12-12T16:36:38.926528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
3.4%
6000 7
 
1.3%
1000 7
 
1.3%
2000 5
 
0.9%
4 4
 
0.7%
3967 4
 
0.7%
1785 3
 
0.6%
15868 3
 
0.6%
8000 3
 
0.6%
11000 3
 
0.6%
Other values (436) 479
89.4%
ValueCountFrequency (%)
0 18
3.4%
1 1
 
0.2%
2 2
 
0.4%
3 2
 
0.4%
4 4
 
0.7%
5 1
 
0.2%
6 2
 
0.4%
7 1
 
0.2%
8 2
 
0.4%
11 2
 
0.4%
ValueCountFrequency (%)
45250000 1
0.2%
43380000 1
0.2%
36968000 1
0.2%
32963000 1
0.2%
32142000 1
0.2%
23227000 1
0.2%
21250000 1
0.2%
17390000 1
0.2%
16340000 1
0.2%
12000000 1
0.2%

인구수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)15.1%
Missing5
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean74.998117
Minimum0
Maximum3813
Zeros435
Zeros (%)81.2%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-12-12T16:36:39.077841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile292.5
Maximum3813
Range3813
Interquartile range (IQR)0

Descriptive statistics

Standard deviation375.85456
Coefficient of variation (CV)5.01152
Kurtosis53.263895
Mean74.998117
Median Absolute Deviation (MAD)0
Skewness7.0327649
Sum39824
Variance141266.65
MonotonicityNot monotonic
2023-12-12T16:36:39.238816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 435
81.2%
34 4
 
0.7%
48 3
 
0.6%
20 2
 
0.4%
15 2
 
0.4%
39 2
 
0.4%
2 2
 
0.4%
21 2
 
0.4%
28 2
 
0.4%
2926 2
 
0.4%
Other values (70) 75
 
14.0%
(Missing) 5
 
0.9%
ValueCountFrequency (%)
0 435
81.2%
1 1
 
0.2%
2 2
 
0.4%
3 2
 
0.4%
6 2
 
0.4%
9 1
 
0.2%
10 1
 
0.2%
11 1
 
0.2%
13 1
 
0.2%
14 1
 
0.2%
ValueCountFrequency (%)
3813 1
0.2%
3488 1
0.2%
2926 2
0.4%
2593 1
0.2%
2325 1
0.2%
2250 1
0.2%
2215 1
0.2%
1662 1
0.2%
1648 1
0.2%
1417 1
0.2%

가구수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)13.7%
Missing3
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean34.045028
Minimum0
Maximum1809
Zeros437
Zeros (%)81.5%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-12-12T16:36:39.388521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile123.8
Maximum1809
Range1809
Interquartile range (IQR)0

Descriptive statistics

Standard deviation166.94723
Coefficient of variation (CV)4.9037184
Kurtosis53.088307
Mean34.045028
Median Absolute Deviation (MAD)0
Skewness6.9543451
Sum18146
Variance27871.378
MonotonicityNot monotonic
2023-12-12T16:36:39.520257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 437
81.5%
15 6
 
1.1%
9 5
 
0.9%
29 4
 
0.7%
1 3
 
0.6%
24 3
 
0.6%
13 3
 
0.6%
38 2
 
0.4%
17 2
 
0.4%
11 2
 
0.4%
Other values (63) 66
 
12.3%
(Missing) 3
 
0.6%
ValueCountFrequency (%)
0 437
81.5%
1 3
 
0.6%
2 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
5 2
 
0.4%
7 1
 
0.2%
8 2
 
0.4%
9 5
 
0.9%
11 2
 
0.4%
ValueCountFrequency (%)
1809 1
0.2%
1415 1
0.2%
1224 1
0.2%
1202 1
0.2%
1177 1
0.2%
1147 1
0.2%
1045 1
0.2%
835 1
0.2%
831 1
0.2%
661 1
0.2%

해안선길이
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct163
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.144769
Minimum0
Maximum12545
Zeros345
Zeros (%)64.4%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-12-12T16:36:39.711311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.36125
95-th percentile13.475
Maximum12545
Range12545
Interquartile range (IQR)0.36125

Descriptive statistics

Standard deviation541.83884
Coefficient of variation (CV)20.72456
Kurtosis535.61836
Mean26.144769
Median Absolute Deviation (MAD)0
Skewness23.139376
Sum14013.596
Variance293589.33
MonotonicityNot monotonic
2023-12-12T16:36:39.873405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 345
64.4%
3.0 4
 
0.7%
0.2 3
 
0.6%
8.0 3
 
0.6%
5.5 3
 
0.6%
10.0 3
 
0.6%
5.0 2
 
0.4%
1.0 2
 
0.4%
9.9 2
 
0.4%
4.6 2
 
0.4%
Other values (153) 167
31.2%
ValueCountFrequency (%)
0.0 345
64.4%
0.018 1
 
0.2%
0.027 1
 
0.2%
0.043 1
 
0.2%
0.046 1
 
0.2%
0.05 1
 
0.2%
0.052 1
 
0.2%
0.06 1
 
0.2%
0.07 1
 
0.2%
0.079 1
 
0.2%
ValueCountFrequency (%)
12545.0 1
0.2%
116.0 1
0.2%
89.84 1
0.2%
86.6 1
0.2%
65.5 1
0.2%
59.15 1
0.2%
45.0 1
0.2%
44.2 1
0.2%
42.0 2
0.4%
41.91 1
0.2%

Interactions

2023-12-12T16:36:36.219970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:33.787633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:34.648455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:35.156558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:35.725049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:36.320595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:33.952904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:34.743974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:35.268750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:35.829819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:36.415696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:34.043318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:34.868424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:35.387556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:35.920992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:36.534316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:34.147808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:34.969257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:35.508356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:36.021473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:36.649105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:34.555782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:35.069258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:35.628479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:36.119273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:36:39.995772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호지구명면적인구수가구수해안선길이
번호1.0000.8910.1510.1160.1650.021
지구명0.8911.0000.0000.0000.0000.093
면적0.1510.0001.0000.8440.9360.000
인구수0.1160.0000.8441.0000.9180.369
가구수0.1650.0000.9360.9181.0001.000
해안선길이0.0210.0930.0000.3691.0001.000
2023-12-12T16:36:40.161497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호면적인구수가구수해안선길이지구명
번호1.000-0.480-0.226-0.227-0.1690.620
면적-0.4801.0000.4650.4600.3180.000
인구수-0.2260.4651.0000.9910.7280.000
가구수-0.2270.4600.9911.0000.7270.000
해안선길이-0.1690.3180.7280.7271.0000.082
지구명0.6200.0000.0000.0000.0821.000

Missing values

2023-12-12T16:36:36.780919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:36:36.896317image/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.
2023-12-12T16:36:36.988773image/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

번호도서명지구명위치면적인구수가구수해안선길이
013구도소안청산지구전라남도 완도군 소안면 횡간리 18727700069292.4
114소안도소안청산지구전라남도 완도군 소안면 비자리 193232270002593122442.0
215당사도소안청산지구전라남도 완도군 소안면 당사리 산 91-12140200047278.0
361소분점도 2원북지구충청남도 태안군 원북면 방갈리2815000.193
483화기서소근지구충청남도 태안군 근흥면 신진도리 204642000.31
584송도소근지구충청남도 태안군 근흥면 정죽리 39415265001.5
685죽통바위소근지구충청남도 태안군 근흥면 도황리1558000.167
786비안목남면지구충청남도 태안군 남면 몽산리 22312136001.575
887검은바위남면지구충청남도 태안군 남면 원청리659000.097
988노적봉1남면지구충청남도 태안군 남면 신온리564000.095
번호도서명지구명위치면적인구수가구수해안선길이
526449독립문바위5소근지구충청남도 태안군 근흥면 가의도리120000.043
527450무억도2소근지구충청남도 태안군 근흥면 신진도리161000.05
528451무명섬2소근지구충청남도 태안군 소원면 파도리 산 2171240000.182
529452무명섬3소근지구충청남도 태안군 소원면 파도리169000.06
530453무명섬4소근지구충청남도 태안군 소원면 파도리 21765000.027
531455잠섬소근지구충청남도 태안군 근흥면 도황리1777000.151
532456안목도2남면지구충청남도 태안군 남면 몽산리 산 661-61076000.12
533457안목도3남면지구충청남도 태안군 남면 몽산리 산 661-61255000.163
534458안목도4남면지구충청남도 태안군 남면 몽산리 산 661-61907000.165
535459안목도5남면지구충청남도 태안군 남면 몽산리785000.1