Overview

Dataset statistics

Number of variables8
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory71.1 B

Variable types

Numeric2
Categorical2
Text4

Dataset

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

Alerts

연번 is highly overall correlated with 시군 and 1 other fieldsHigh correlation
시군 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
개장기간 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 has unique valuesUnique
해수욕장명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:46:12.096767
Analysis finished2023-12-10 23:46:12.993853
Duration0.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.5
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T08:46:13.055404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.25
Q17.25
median13.5
Q319.75
95-th percentile24.75
Maximum26
Range25
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation7.6485293
Coefficient of variation (CV)0.56655772
Kurtosis-1.2
Mean13.5
Median Absolute Deviation (MAD)6.5
Skewness0
Sum351
Variance58.5
MonotonicityStrictly increasing
2023-12-11T08:46:13.193600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 1
 
3.8%
15 1
 
3.8%
26 1
 
3.8%
25 1
 
3.8%
24 1
 
3.8%
23 1
 
3.8%
22 1
 
3.8%
21 1
 
3.8%
20 1
 
3.8%
19 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
1 1
3.8%
2 1
3.8%
3 1
3.8%
4 1
3.8%
5 1
3.8%
6 1
3.8%
7 1
3.8%
8 1
3.8%
9 1
3.8%
10 1
3.8%
ValueCountFrequency (%)
26 1
3.8%
25 1
3.8%
24 1
3.8%
23 1
3.8%
22 1
3.8%
21 1
3.8%
20 1
3.8%
19 1
3.8%
18 1
3.8%
17 1
3.8%

시군
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size340.0 B
거제
13 
통영
6 
남해
6 
사천
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)3.8%

Sample

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

Common Values

ValueCountFrequency (%)
거제 13
50.0%
통영 6
23.1%
남해 6
23.1%
사천 1
 
3.8%

Length

2023-12-11T08:46:13.312978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:46:13.431020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거제 13
50.0%
통영 6
23.1%
남해 6
23.1%
사천 1
 
3.8%

해수욕장명
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-11T08:46:13.628086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length2
Mean length4.0769231
Min length2

Characters and Unicode

Total characters106
Distinct characters71
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
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 row통영공설
2nd row비진도 산호빛 해변
3rd row사량 대항
4th row덕동
5th row봉암 몽돌
ValueCountFrequency (%)
해변 4
 
10.8%
통영공설 1
 
2.7%
상주 1
 
2.7%
황포 1
 
2.7%
옆개(물안 1
 
2.7%
덕원 1
 
2.7%
여차 1
 
2.7%
함목 1
 
2.7%
죽림 1
 
2.7%
은모래비치 1
 
2.7%
Other values (24) 24
64.9%
2023-12-11T08:46:13.945250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11
 
10.4%
해 4
 
3.8%
변 4
 
3.8%
대 3
 
2.8%
포 3
 
2.8%
덕 3
 
2.8%
사 3
 
2.8%
모 2
 
1.9%
래 2
 
1.9%
구 2
 
1.9%
Other values (61) 69
65.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 92
86.8%
Space Separator 11
 
10.4%
Close Punctuation 1
 
0.9%
Other Punctuation 1
 
0.9%
Open Punctuation 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
해 4
 
4.3%
변 4
 
4.3%
대 3
 
3.3%
포 3
 
3.3%
덕 3
 
3.3%
사 3
 
3.3%
모 2
 
2.2%
래 2
 
2.2%
구 2
 
2.2%
주 2
 
2.2%
Other values (57) 64
69.6%
Space Separator
ValueCountFrequency (%)
11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 92
86.8%
Common 14
 
13.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
해 4
 
4.3%
변 4
 
4.3%
대 3
 
3.3%
포 3
 
3.3%
덕 3
 
3.3%
사 3
 
3.3%
모 2
 
2.2%
래 2
 
2.2%
구 2
 
2.2%
주 2
 
2.2%
Other values (57) 64
69.6%
Common
ValueCountFrequency (%)
11
78.6%
) 1
 
7.1%
· 1
 
7.1%
( 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 92
86.8%
ASCII 13
 
12.3%
None 1
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11
84.6%
) 1
 
7.7%
( 1
 
7.7%
Hangul
ValueCountFrequency (%)
해 4
 
4.3%
변 4
 
4.3%
대 3
 
3.3%
포 3
 
3.3%
덕 3
 
3.3%
사 3
 
3.3%
모 2
 
2.2%
래 2
 
2.2%
구 2
 
2.2%
주 2
 
2.2%
Other values (57) 64
69.6%
None
ValueCountFrequency (%)
· 1
100.0%

규모(㎡)
Real number (ℝ)

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51132.346
Minimum1000
Maximum546392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T08:46:14.070747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile3975
Q19375
median15600
Q334875
95-th percentile137187.25
Maximum546392
Range545392
Interquartile range (IQR)25500

Descriptive statistics

Standard deviation107469.44
Coefficient of variation (CV)2.1017898
Kurtosis19.602708
Mean51132.346
Median Absolute Deviation (MAD)9300
Skewness4.2413545
Sum1329441
Variance1.1549681 × 1010
MonotonicityNot monotonic
2023-12-11T08:46:14.180427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10500 2
 
7.7%
6000 2
 
7.7%
80000 1
 
3.8%
15000 1
 
3.8%
27000 1
 
3.8%
13000 1
 
3.8%
104749 1
 
3.8%
546392 1
 
3.8%
15900 1
 
3.8%
13500 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
1000 1
3.8%
3300 1
3.8%
6000 2
7.7%
6600 1
3.8%
7500 1
3.8%
9000 1
3.8%
10500 2
7.7%
13000 1
3.8%
13500 1
3.8%
15000 1
3.8%
ValueCountFrequency (%)
546392 1
3.8%
148000 1
3.8%
104749 1
3.8%
92700 1
3.8%
80000 1
3.8%
66000 1
3.8%
36000 1
3.8%
31500 1
3.8%
27000 1
3.8%
24000 1
3.8%

개장기간
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Memory size340.0 B
2011.7.1. ~ 8.21.
13 
2011.7.8. ~ 8.21.
8 
2011.7.9. ~ 8.21.
3 
2011.7.6. ~ 8.21.
 
1
2011.7.14.~ 8.21.
 
1

Length

Max length17
Median length17
Mean length17
Min length17

Unique

Unique2 ?
Unique (%)7.7%

Sample

1st row2011.7.8. ~ 8.21.
2nd row2011.7.8. ~ 8.21.
3rd row2011.7.8. ~ 8.21.
4th row2011.7.8. ~ 8.21.
5th row2011.7.8. ~ 8.21.

Common Values

ValueCountFrequency (%)
2011.7.1. ~ 8.21. 13
50.0%
2011.7.8. ~ 8.21. 8
30.8%
2011.7.9. ~ 8.21. 3
 
11.5%
2011.7.6. ~ 8.21. 1
 
3.8%
2011.7.14.~ 8.21. 1
 
3.8%

Length

2023-12-11T08:46:14.332528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:46:14.453130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
8.21 26
33.8%
25
32.5%
2011.7.1 13
16.9%
2011.7.8 8
 
10.4%
2011.7.9 3
 
3.9%
2011.7.6 1
 
1.3%
2011.7.14 1
 
1.3%

2009년 방문객 수(명)
Text

Distinct22
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-11T08:46:14.619712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.2692308
Min length1

Characters and Unicode

Total characters111
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

Unique21 ?
Unique (%)80.8%

Sample

1st row25380
2nd row32150
3rd row12340
4th row-
5th row-
ValueCountFrequency (%)
5
19.2%
25380 1
 
3.8%
7340 1
 
3.8%
24630 1
 
3.8%
79645 1
 
3.8%
316686 1
 
3.8%
11650 1
 
3.8%
13110 1
 
3.8%
21540 1
 
3.8%
5398 1
 
3.8%
Other values (12) 12
46.2%
2023-12-11T08:46:14.896570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 19
17.1%
5 16
14.4%
3 16
14.4%
0 15
13.5%
4 10
9.0%
2 8
7.2%
6 8
7.2%
7 7
 
6.3%
- 5
 
4.5%
9 4
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 106
95.5%
Dash Punctuation 5
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 19
17.9%
5 16
15.1%
3 16
15.1%
0 15
14.2%
4 10
9.4%
2 8
7.5%
6 8
7.5%
7 7
 
6.6%
9 4
 
3.8%
8 3
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 111
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 19
17.1%
5 16
14.4%
3 16
14.4%
0 15
13.5%
4 10
9.0%
2 8
7.2%
6 8
7.2%
7 7
 
6.3%
- 5
 
4.5%
9 4
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 19
17.1%
5 16
14.4%
3 16
14.4%
0 15
13.5%
4 10
9.0%
2 8
7.2%
6 8
7.2%
7 7
 
6.3%
- 5
 
4.5%
9 4
 
3.6%

2010년 방문객 수(명)
Text

Distinct22
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-11T08:46:15.049002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5.5
Mean length4.3076923
Min length1

Characters and Unicode

Total characters112
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

Unique21 ?
Unique (%)80.8%

Sample

1st row29500
2nd row40000
3rd row19500
4th row-
5th row-
ValueCountFrequency (%)
5
19.2%
29500 1
 
3.8%
7465 1
 
3.8%
24898 1
 
3.8%
96948 1
 
3.8%
301860 1
 
3.8%
11900 1
 
3.8%
13000 1
 
3.8%
21015 1
 
3.8%
8170 1
 
3.8%
Other values (12) 12
46.2%
2023-12-11T08:46:15.324395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 30
26.8%
1 13
11.6%
2 10
 
8.9%
5 10
 
8.9%
7 9
 
8.0%
8 9
 
8.0%
3 8
 
7.1%
9 7
 
6.2%
4 6
 
5.4%
- 5
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107
95.5%
Dash Punctuation 5
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30
28.0%
1 13
12.1%
2 10
 
9.3%
5 10
 
9.3%
7 9
 
8.4%
8 9
 
8.4%
3 8
 
7.5%
9 7
 
6.5%
4 6
 
5.6%
6 5
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 112
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30
26.8%
1 13
11.6%
2 10
 
8.9%
5 10
 
8.9%
7 9
 
8.0%
8 9
 
8.0%
3 8
 
7.1%
9 7
 
6.2%
4 6
 
5.4%
- 5
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30
26.8%
1 13
11.6%
2 10
 
8.9%
5 10
 
8.9%
7 9
 
8.0%
8 9
 
8.0%
3 8
 
7.1%
9 7
 
6.2%
4 6
 
5.4%
- 5
 
4.5%

2011년 방문객 수(명)
Text

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-11T08:46:15.505396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.3461538
Min length1

Characters and Unicode

Total characters113
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

Unique24 ?
Unique (%)92.3%

Sample

1st row6980
2nd row9250
3rd row8993
4th row10950
5th row7740
ValueCountFrequency (%)
2
 
7.7%
5055 1
 
3.8%
26270 1
 
3.8%
24120 1
 
3.8%
225650 1
 
3.8%
600120 1
 
3.8%
5530 1
 
3.8%
5292 1
 
3.8%
9010 1
 
3.8%
5597 1
 
3.8%
Other values (15) 15
57.7%
2023-12-11T08:46:15.808428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 20
17.7%
5 16
14.2%
2 13
11.5%
9 11
9.7%
1 11
9.7%
4 10
8.8%
7 9
8.0%
8 8
 
7.1%
6 8
 
7.1%
3 5
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111
98.2%
Dash Punctuation 2
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20
18.0%
5 16
14.4%
2 13
11.7%
9 11
9.9%
1 11
9.9%
4 10
9.0%
7 9
8.1%
8 8
 
7.2%
6 8
 
7.2%
3 5
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 113
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20
17.7%
5 16
14.2%
2 13
11.5%
9 11
9.7%
1 11
9.7%
4 10
8.8%
7 9
8.0%
8 8
 
7.1%
6 8
 
7.1%
3 5
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20
17.7%
5 16
14.2%
2 13
11.5%
9 11
9.7%
1 11
9.7%
4 10
8.8%
7 9
8.0%
8 8
 
7.1%
6 8
 
7.1%
3 5
 
4.4%

Interactions

2023-12-11T08:46:12.571027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:12.421144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:12.665358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:12.497371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:46:15.930155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군해수욕장명규모(㎡)개장기간2009년 방문객 수(명)2010년 방문객 수(명)2011년 방문객 수(명)
연번1.0000.8881.0000.4060.9260.8460.8461.000
시군0.8881.0001.0000.3700.7590.7880.7881.000
해수욕장명1.0001.0001.0001.0001.0001.0001.0001.000
규모(㎡)0.4060.3701.0001.0000.5691.0001.0001.000
개장기간0.9260.7591.0000.5691.0000.7840.7841.000
2009년 방문객 수(명)0.8460.7881.0001.0000.7841.0001.0001.000
2010년 방문객 수(명)0.8460.7881.0001.0000.7841.0001.0001.000
2011년 방문객 수(명)1.0001.0001.0001.0001.0001.0001.0001.000
2023-12-11T08:46:16.073487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개장기간시군
개장기간1.0000.688
시군0.6881.000
2023-12-11T08:46:16.159697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번규모(㎡)시군개장기간
연번1.0000.1740.6800.592
규모(㎡)0.1741.0000.1630.480
시군0.6800.1631.0000.688
개장기간0.5920.4800.6881.000

Missing values

2023-12-11T08:46:12.787125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:46:12.924927image/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

연번시군해수욕장명규모(㎡)개장기간2009년 방문객 수(명)2010년 방문객 수(명)2011년 방문객 수(명)
01통영통영공설220002011.7.8. ~ 8.21.25380295006980
12통영비진도 산호빛 해변75002011.7.8. ~ 8.21.32150400009250
23통영사량 대항60002011.7.8. ~ 8.21.12340195008993
34통영덕동33002011.7.8. ~ 8.21.--10950
45통영봉암 몽돌240002011.7.8. ~ 8.21.--7740
56통영연대10002011.7.8. ~ 8.21.--761
67사천남일대660002011.7.8. ~ 8.21.155145200310180370
78거제명사315002011.7.1. ~ 8.21.531505076525440
89거제학동 흑진주몽돌 해변1480002011.7.1. ~ 8.21.339745307315144405
910거제구조라927002011.7.1. ~ 8.21.30131524780574890
연번시군해수욕장명규모(㎡)개장기간2009년 방문객 수(명)2010년 방문객 수(명)2011년 방문객 수(명)
1617거제덕원90002011.7.1. ~ 8.21.539881705597
1718거제여차360002011.7.1. ~ 8.21.21540210159010
1819거제함목135002011.7.1. ~ 8.21.13110130005292
1920거제죽림159002011.7.1. ~ 8.21.11650119005530
2021남해상주 은모래비치5463922011.7.6. ~ 8.21.316686301860600120
2122남해송정 솔바람 해변1047492011.7.8. ~ 8.21.7964596948225650
2223남해사촌130002011.7.14.~ 8.21.246302489824120
2324남해두곡·월포270002011.7.9. ~ 8.21.271342821026270
2425남해선구105002011.7.9. ~ 8.21.---
2526남해설리150002011.7.9. ~ 8.21.---