Overview

Dataset statistics

Number of variables11
Number of observations27
Missing cells5
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory94.9 B

Variable types

Categorical4
Text5
Numeric2

Dataset

Description경남도내 연도별 해수욕장 방문 현황입니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3034255

Alerts

유영가능면적(㎡) is highly overall correlated with 2015년 방문객 수(명) and 1 other fieldsHigh correlation
2015년 방문객 수(명) is highly overall correlated with 유영가능면적(㎡) and 2 other fieldsHigh correlation
시군명 is highly overall correlated with 2019년 기준 폐장일 and 1 other fieldsHigh correlation
2019년 기준 개장일 is highly overall correlated with 유영가능면적(㎡) and 2 other fieldsHigh correlation
2019년 기준 폐장일 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
비고 is highly overall correlated with 2015년 방문객 수(명) and 1 other fieldsHigh correlation
비고 is highly imbalanced (71.3%)Imbalance
2015년 방문객 수(명) has 2 (7.4%) missing valuesMissing
2016 방문객 수(명) has 2 (7.4%) missing valuesMissing
2017 방문객 수(명) has 1 (3.7%) missing valuesMissing
해수욕장명 has unique valuesUnique
2018 방문객 수(명) has unique valuesUnique
2019 방문객 수(명) has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:46:22.609462
Analysis finished2023-12-10 23:46:23.950240
Duration1.34 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size348.0 B
거 제
17 
남 해
5 
통 영
3 
창원
 
1
사 천
 
1

Length

Max length3
Median length3
Mean length2.962963
Min length2

Unique

Unique2 ?
Unique (%)7.4%

Sample

1st row창원
2nd row통 영
3rd row통 영
4th row통 영
5th row사 천

Common Values

ValueCountFrequency (%)
거 제 17
63.0%
남 해 5
 
18.5%
통 영 3
 
11.1%
창원 1
 
3.7%
사 천 1
 
3.7%

Length

2023-12-11T08:46:24.029061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:46:24.158009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거 17
32.1%
제 17
32.1%
남 5
 
9.4%
해 5
 
9.4%
통 3
 
5.7%
영 3
 
5.7%
창원 1
 
1.9%
사 1
 
1.9%
천 1
 
1.9%

해수욕장명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-11T08:46:24.417713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length3.5925926
Min length2

Characters and Unicode

Total characters97
Distinct characters71
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

Unique27 ?
Unique (%)100.0%

Sample

1st row광암
2nd row수륙
3rd row비진도 산호빛해변
4th row사량 대항
5th row남일대
ValueCountFrequency (%)
광암 1
 
3.1%
수륙 1
 
3.1%
두곡 1
 
3.1%
사촌 1
 
3.1%
설리 1
 
3.1%
송정솔바람해변 1
 
3.1%
상주은모래비치 1
 
3.1%
죽림 1
 
3.1%
덕포 1
 
3.1%
흥남 1
 
3.1%
Other values (22) 22
68.8%
2023-12-11T08:46:24.837601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5
 
5.2%
사 4
 
4.1%
변 4
 
4.1%
해 4
 
4.1%
포 3
 
3.1%
덕 2
 
2.1%
치 2
 
2.1%
구 2
 
2.1%
남 2
 
2.1%
주 2
 
2.1%
Other values (61) 67
69.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 90
92.8%
Space Separator 5
 
5.2%
Close Punctuation 1
 
1.0%
Open Punctuation 1
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 4
 
4.4%
변 4
 
4.4%
해 4
 
4.4%
포 3
 
3.3%
덕 2
 
2.2%
치 2
 
2.2%
구 2
 
2.2%
남 2
 
2.2%
주 2
 
2.2%
대 2
 
2.2%
Other values (58) 63
70.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 90
92.8%
Common 7
 
7.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 4
 
4.4%
변 4
 
4.4%
해 4
 
4.4%
포 3
 
3.3%
덕 2
 
2.2%
치 2
 
2.2%
구 2
 
2.2%
남 2
 
2.2%
주 2
 
2.2%
대 2
 
2.2%
Other values (58) 63
70.0%
Common
ValueCountFrequency (%)
5
71.4%
) 1
 
14.3%
( 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 90
92.8%
ASCII 7
 
7.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5
71.4%
) 1
 
14.3%
( 1
 
14.3%
Hangul
ValueCountFrequency (%)
사 4
 
4.4%
변 4
 
4.4%
해 4
 
4.4%
포 3
 
3.3%
덕 2
 
2.2%
치 2
 
2.2%
구 2
 
2.2%
남 2
 
2.2%
주 2
 
2.2%
대 2
 
2.2%
Other values (58) 63
70.0%

유영가능면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49503.481
Minimum1100
Maximum546392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T08:46:24.963437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1100
5-th percentile4300
Q17050
median15900
Q333750
95-th percentile135024.7
Maximum546392
Range545292
Interquartile range (IQR)26700

Descriptive statistics

Standard deviation105745.56
Coefficient of variation (CV)2.1361236
Kurtosis20.305
Mean49503.481
Median Absolute Deviation (MAD)9900
Skewness4.3141914
Sum1336594
Variance1.1182123 × 1010
MonotonicityNot monotonic
2023-12-11T08:46:25.076534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
6000 3
 
11.1%
25653 1
 
3.7%
4000 1
 
3.7%
27000 1
 
3.7%
13000 1
 
3.7%
19200 1
 
3.7%
104749 1
 
3.7%
546392 1
 
3.7%
15900 1
 
3.7%
18000 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
1100 1
 
3.7%
4000 1
 
3.7%
5000 1
 
3.7%
6000 3
11.1%
6600 1
 
3.7%
7500 1
 
3.7%
9000 1
 
3.7%
10500 1
 
3.7%
13000 1
 
3.7%
13500 1
 
3.7%
ValueCountFrequency (%)
546392 1
3.7%
148000 1
3.7%
104749 1
3.7%
92700 1
3.7%
80000 1
3.7%
66000 1
3.7%
36000 1
3.7%
31500 1
3.7%
27000 1
3.7%
25653 1
3.7%

2015년 방문객 수(명)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)100.0%
Missing2
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean30589.08
Minimum1448
Maximum202066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T08:46:25.182562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1448
5-th percentile2014.6
Q16920
median14594
Q343400
95-th percentile88108
Maximum202066
Range200618
Interquartile range (IQR)36480

Descriptive statistics

Standard deviation42978.982
Coefficient of variation (CV)1.4050433
Kurtosis10.507617
Mean30589.08
Median Absolute Deviation (MAD)9459
Skewness2.9584218
Sum764727
Variance1.8471929 × 109
MonotonicityNot monotonic
2023-12-11T08:46:25.295121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
8373 1
 
3.7%
14594 1
 
3.7%
24798 1
 
3.7%
59975 1
 
3.7%
202066 1
 
3.7%
1475 1
 
3.7%
9650 1
 
3.7%
46675 1
 
3.7%
9101 1
 
3.7%
33334 1
 
3.7%
Other values (15) 15
55.6%
(Missing) 2
 
7.4%
ValueCountFrequency (%)
1448 1
3.7%
1475 1
3.7%
4173 1
3.7%
5135 1
3.7%
5866 1
3.7%
6266 1
3.7%
6920 1
3.7%
7311 1
3.7%
8354 1
3.7%
8373 1
3.7%
ValueCountFrequency (%)
202066 1
3.7%
93130 1
3.7%
68020 1
3.7%
59975 1
3.7%
48990 1
3.7%
46675 1
3.7%
43400 1
3.7%
33334 1
3.7%
24798 1
3.7%
21790 1
3.7%

2016 방문객 수(명)
Text

MISSING 

Distinct25
Distinct (%)100.0%
Missing2
Missing (%)7.4%
Memory size348.0 B
2023-12-11T08:46:25.479031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.56
Min length3

Characters and Unicode

Total characters114
Distinct characters13
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

Unique25 ?
Unique (%)100.0%

Sample

1st row12457
2nd row16085
3rd row2739
4th row43893
5th row77460
ValueCountFrequency (%)
12457 1
 
4.0%
9906 1
 
4.0%
12599 1
 
4.0%
109492 1
 
4.0%
193816 1
 
4.0%
비개장 1
 
4.0%
7988 1
 
4.0%
33060 1
 
4.0%
6276 1
 
4.0%
41390 1
 
4.0%
Other values (15) 15
60.0%
2023-12-11T08:46:25.814487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 16
14.0%
1 13
11.4%
0 13
11.4%
3 13
11.4%
7 12
10.5%
6 11
9.6%
4 10
8.8%
8 9
7.9%
2 7
6.1%
5 7
6.1%
Other values (3) 3
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111
97.4%
Other Letter 3
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 16
14.4%
1 13
11.7%
0 13
11.7%
3 13
11.7%
7 12
10.8%
6 11
9.9%
4 10
9.0%
8 9
8.1%
2 7
6.3%
5 7
6.3%
Other Letter
ValueCountFrequency (%)
비 1
33.3%
개 1
33.3%
장 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 111
97.4%
Hangul 3
 
2.6%

Most frequent character per script

Common
ValueCountFrequency (%)
9 16
14.4%
1 13
11.7%
0 13
11.7%
3 13
11.7%
7 12
10.8%
6 11
9.9%
4 10
9.0%
8 9
8.1%
2 7
6.3%
5 7
6.3%
Hangul
ValueCountFrequency (%)
비 1
33.3%
개 1
33.3%
장 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111
97.4%
Hangul 3
 
2.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 16
14.4%
1 13
11.7%
0 13
11.7%
3 13
11.7%
7 12
10.8%
6 11
9.9%
4 10
9.0%
8 9
8.1%
2 7
6.3%
5 7
6.3%
Hangul
ValueCountFrequency (%)
비 1
33.3%
개 1
33.3%
장 1
33.3%

2017 방문객 수(명)
Text

MISSING 

Distinct26
Distinct (%)100.0%
Missing1
Missing (%)3.7%
Memory size348.0 B
2023-12-11T08:46:26.006675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.6538462
Min length3

Characters and Unicode

Total characters121
Distinct characters13
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

Unique26 ?
Unique (%)100.0%

Sample

1st row15520
2nd row13477
3rd row3018
4th row35305
5th row55500
ValueCountFrequency (%)
15520 1
 
3.8%
13477 1
 
3.8%
6316 1
 
3.8%
51282 1
 
3.8%
103851 1
 
3.8%
166944 1
 
3.8%
비개장 1
 
3.8%
14814 1
 
3.8%
38635 1
 
3.8%
2685 1
 
3.8%
Other values (16) 16
61.5%
2023-12-11T08:46:26.305107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 18
14.9%
1 17
14.0%
3 13
10.7%
0 12
9.9%
6 12
9.9%
2 10
8.3%
4 10
8.3%
7 10
8.3%
8 10
8.3%
9 6
 
5.0%
Other values (3) 3
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 118
97.5%
Other Letter 3
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 18
15.3%
1 17
14.4%
3 13
11.0%
0 12
10.2%
6 12
10.2%
2 10
8.5%
4 10
8.5%
7 10
8.5%
8 10
8.5%
9 6
 
5.1%
Other Letter
ValueCountFrequency (%)
비 1
33.3%
개 1
33.3%
장 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 118
97.5%
Hangul 3
 
2.5%

Most frequent character per script

Common
ValueCountFrequency (%)
5 18
15.3%
1 17
14.4%
3 13
11.0%
0 12
10.2%
6 12
10.2%
2 10
8.5%
4 10
8.5%
7 10
8.5%
8 10
8.5%
9 6
 
5.1%
Hangul
ValueCountFrequency (%)
비 1
33.3%
개 1
33.3%
장 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118
97.5%
Hangul 3
 
2.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 18
15.3%
1 17
14.4%
3 13
11.0%
0 12
10.2%
6 12
10.2%
2 10
8.5%
4 10
8.5%
7 10
8.5%
8 10
8.5%
9 6
 
5.1%
Hangul
ValueCountFrequency (%)
비 1
33.3%
개 1
33.3%
장 1
33.3%

2018 방문객 수(명)
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-11T08:46:26.498147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.5925926
Min length3

Characters and Unicode

Total characters124
Distinct characters13
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

Unique27 ?
Unique (%)100.0%

Sample

1st row29350
2nd row12953
3rd row12540
4th row4307
5th row28100
ValueCountFrequency (%)
29350 1
 
3.7%
11592 1
 
3.7%
4830 1
 
3.7%
9068 1
 
3.7%
69906 1
 
3.7%
115272 1
 
3.7%
비개장 1
 
3.7%
14925 1
 
3.7%
32022 1
 
3.7%
5310 1
 
3.7%
Other values (17) 17
63.0%
2023-12-11T08:46:26.858647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 20
16.1%
5 18
14.5%
2 16
12.9%
1 14
11.3%
3 13
10.5%
9 11
8.9%
4 9
7.3%
7 8
 
6.5%
6 7
 
5.6%
8 5
 
4.0%
Other values (3) 3
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
97.6%
Other Letter 3
 
2.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20
16.5%
5 18
14.9%
2 16
13.2%
1 14
11.6%
3 13
10.7%
9 11
9.1%
4 9
7.4%
7 8
 
6.6%
6 7
 
5.8%
8 5
 
4.1%
Other Letter
ValueCountFrequency (%)
비 1
33.3%
개 1
33.3%
장 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 121
97.6%
Hangul 3
 
2.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20
16.5%
5 18
14.9%
2 16
13.2%
1 14
11.6%
3 13
10.7%
9 11
9.1%
4 9
7.4%
7 8
 
6.6%
6 7
 
5.8%
8 5
 
4.1%
Hangul
ValueCountFrequency (%)
비 1
33.3%
개 1
33.3%
장 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
97.6%
Hangul 3
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20
16.5%
5 18
14.9%
2 16
13.2%
1 14
11.6%
3 13
10.7%
9 11
9.1%
4 9
7.4%
7 8
 
6.6%
6 7
 
5.8%
8 5
 
4.1%
Hangul
ValueCountFrequency (%)
비 1
33.3%
개 1
33.3%
장 1
33.3%

2019 방문객 수(명)
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-11T08:46:27.058161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.6296296
Min length3

Characters and Unicode

Total characters125
Distinct characters13
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

Unique27 ?
Unique (%)100.0%

Sample

1st row39380
2nd row14002
3rd row10297
4th row3583
5th row42266
ValueCountFrequency (%)
39380 1
 
3.7%
10567 1
 
3.7%
10410 1
 
3.7%
8945 1
 
3.7%
57746 1
 
3.7%
122133 1
 
3.7%
비개장 1
 
3.7%
13125 1
 
3.7%
29579 1
 
3.7%
4636 1
 
3.7%
Other values (17) 17
63.0%
2023-12-11T08:46:27.427753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16
12.8%
3 14
11.2%
1 14
11.2%
2 14
11.2%
5 14
11.2%
4 12
9.6%
6 12
9.6%
7 10
8.0%
8 9
7.2%
9 7
5.6%
Other values (3) 3
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 122
97.6%
Other Letter 3
 
2.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16
13.1%
3 14
11.5%
1 14
11.5%
2 14
11.5%
5 14
11.5%
4 12
9.8%
6 12
9.8%
7 10
8.2%
8 9
7.4%
9 7
5.7%
Other Letter
ValueCountFrequency (%)
비 1
33.3%
개 1
33.3%
장 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 122
97.6%
Hangul 3
 
2.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16
13.1%
3 14
11.5%
1 14
11.5%
2 14
11.5%
5 14
11.5%
4 12
9.8%
6 12
9.8%
7 10
8.2%
8 9
7.4%
9 7
5.7%
Hangul
ValueCountFrequency (%)
비 1
33.3%
개 1
33.3%
장 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122
97.6%
Hangul 3
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16
13.1%
3 14
11.5%
1 14
11.5%
2 14
11.5%
5 14
11.5%
4 12
9.8%
6 12
9.8%
7 10
8.2%
8 9
7.4%
9 7
5.7%
Hangul
ValueCountFrequency (%)
비 1
33.3%
개 1
33.3%
장 1
33.3%

2019년 기준 개장일
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size348.0 B
2019-07-06
18 
2019-07-13
7 
 
 
1
2019-07-05
 
1

Length

Max length10
Median length10
Mean length9.7037037
Min length2

Unique

Unique2 ?
Unique (%)7.4%

Sample

1st row2019-07-06
2nd row2019-07-06
3rd row2019-07-06
4th row2019-07-06
5th row2019-07-06

Common Values

ValueCountFrequency (%)
2019-07-06 18
66.7%
2019-07-13 7
 
25.9%
  1
 
3.7%
2019-07-05 1
 
3.7%

Length

2023-12-11T08:46:27.590876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:46:27.700143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-07-06 18
69.2%
2019-07-13 7
 
26.9%
2019-07-05 1
 
3.8%

2019년 기준 폐장일
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
2019-08-25
16 
2019-08-18
10 
 
 
1

Length

Max length10
Median length10
Mean length9.7037037
Min length2

Unique

Unique1 ?
Unique (%)3.7%

Sample

1st row2019-08-18
2nd row2019-08-18
3rd row2019-08-18
4th row2019-08-18
5th row2019-08-18

Common Values

ValueCountFrequency (%)
2019-08-25 16
59.3%
2019-08-18 10
37.0%
  1
 
3.7%

Length

2023-12-11T08:46:27.806744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:46:28.240086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-08-25 16
61.5%
2019-08-18 10
38.5%

비고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
 
25 
’18년 재개장
 
1
’17년 신규지정
 
1

Length

Max length9
Median length2
Mean length2.4814815
Min length2

Unique

Unique2 ?
Unique (%)7.4%

Sample

1st row’18년 재개장
2nd row 
3rd row 
4th row 
5th row 

Common Values

ValueCountFrequency (%)
  25
92.6%
’18년 재개장 1
 
3.7%
’17년 신규지정 1
 
3.7%

Length

2023-12-11T08:46:28.355506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:46:28.473999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
’18년 1
25.0%
재개장 1
25.0%
’17년 1
25.0%
신규지정 1
25.0%

Interactions

2023-12-11T08:46:23.314916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:23.106025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:23.414997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:23.205233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:46:28.611462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명해수욕장명유영가능면적(㎡)2015년 방문객 수(명)2016 방문객 수(명)2017 방문객 수(명)2018 방문객 수(명)2019 방문객 수(명)2019년 기준 개장일2019년 기준 폐장일비고
시군명1.0001.0000.0000.0001.0001.0001.0001.0000.0000.6700.724
해수욕장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
유영가능면적(㎡)0.0001.0001.0000.9691.0001.0001.0001.0000.8680.0000.000
2015년 방문객 수(명)0.0001.0000.9691.0001.0001.0001.0001.0000.6950.000NaN
2016 방문객 수(명)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000NaN
2017 방문객 수(명)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2018 방문객 수(명)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2019 방문객 수(명)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2019년 기준 개장일0.0001.0000.8680.6951.0001.0001.0001.0001.0000.7240.000
2019년 기준 폐장일0.6701.0000.0000.0001.0001.0001.0001.0000.7241.0000.000
비고0.7241.0000.000NaNNaN1.0001.0001.0000.0000.0001.000
2023-12-11T08:46:28.799933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비고2019년 기준 폐장일2019년 기준 개장일시군명
비고1.0000.0000.0000.683
2019년 기준 폐장일0.0001.0000.7500.612
2019년 기준 개장일0.0000.7501.0000.000
시군명0.6830.6120.0001.000
2023-12-11T08:46:28.938791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유영가능면적(㎡)2015년 방문객 수(명)시군명2019년 기준 개장일2019년 기준 폐장일비고
유영가능면적(㎡)1.0000.5890.0000.5350.0000.000
2015년 방문객 수(명)0.5891.0000.0000.5070.0001.000
시군명0.0000.0001.0000.0000.6120.683
2019년 기준 개장일0.5350.5070.0001.0000.7500.000
2019년 기준 폐장일0.0000.0000.6120.7501.0000.000
비고0.0001.0000.6830.0000.0001.000

Missing values

2023-12-11T08:46:23.586372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:46:23.758578image/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.
2023-12-11T08:46:23.880891image/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

시군명해수욕장명유영가능면적(㎡)2015년 방문객 수(명)2016 방문객 수(명)2017 방문객 수(명)2018 방문객 수(명)2019 방문객 수(명)2019년 기준 개장일2019년 기준 폐장일비고
0창원광암25653<NA><NA><NA>29350393802019-07-062019-08-18’18년 재개장
1통 영수륙75007311124571552012953140022019-07-062019-08-18
2통 영비진도 산호빛해변220008373160851347712540102972019-07-062019-08-18
3통 영사량 대항6000417327393018430735832019-07-062019-08-18
4사 천남일대6600043400438933530528100422662019-07-062019-08-18
5거 제구조라9270068020774605550055737457402019-07-062019-08-25
6거 제망치60001489590451761611590117402019-07-062019-08-25
7거 제와현 모래숲해변1530048990484602655146547355302019-07-062019-08-25
8거 제덕원9000144832315083205544862019-07-132019-08-25
9거 제학동 흑진주몽돌해변14800093130964106496556109486872019-07-062019-08-25
시군명해수욕장명유영가능면적(㎡)2015년 방문객 수(명)2016 방문객 수(명)2017 방문객 수(명)2018 방문객 수(명)2019 방문객 수(명)2019년 기준 개장일2019년 기준 폐장일비고
17거 제농소8000033334413904350730993237652019-07-062019-08-25
18거 제황포6600910162762685531046362019-07-132019-08-25
19거 제흥남1050046675330603863532022295792019-07-062019-08-25
20거 제덕포18000965079881481414925131252019-07-132019-08-25
21거 제죽림159001475비개장비개장비개장비개장
22남 해상주은모래비치5463922020661938161669441152721221332019-07-052019-08-18
23남 해송정솔바람해변1047495997510949210385169906577462019-07-062019-08-18
24남 해설리19200<NA><NA>51282906889452019-07-062019-08-18’17년 신규지정
25남 해사촌13000247981259963164830104102019-07-062019-08-18
26남 해두곡 월포2700014594171787382643651302019-07-062019-08-18