Overview

Dataset statistics

Number of variables7
Number of observations244
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.9 KiB
Average record size in memory58.5 B

Variable types

Numeric2
Categorical4
Text1

Dataset

Description해양수산부에서 관리하고 있는 2021년도 전국에 존재하는 모든 해수욕장을 방문한 이용객 수와 해수욕장을 관리하는 지방자치단체에 대한 정보입니다.
Author해양수산부
URLhttps://www.data.go.kr/data/15100081/fileData.do

Alerts

관리청 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
지자체 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
연번 is highly overall correlated with 지자체 and 2 other fieldsHigh correlation
이용객수 is highly overall correlated with 관리청High correlation
개장일 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
폐장일 is highly overall correlated with 지자체 and 2 other fieldsHigh correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:28:32.144239
Analysis finished2023-12-12 18:28:32.961939
Duration0.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct244
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.5
Minimum1
Maximum244
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-13T03:28:33.022947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13.15
Q161.75
median122.5
Q3183.25
95-th percentile231.85
Maximum244
Range243
Interquartile range (IQR)121.5

Descriptive statistics

Standard deviation70.580923
Coefficient of variation (CV)0.5761708
Kurtosis-1.2
Mean122.5
Median Absolute Deviation (MAD)61
Skewness0
Sum29890
Variance4981.6667
MonotonicityStrictly increasing
2023-12-13T03:28:33.148610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
155 1
 
0.4%
157 1
 
0.4%
158 1
 
0.4%
159 1
 
0.4%
160 1
 
0.4%
161 1
 
0.4%
162 1
 
0.4%
163 1
 
0.4%
164 1
 
0.4%
Other values (234) 234
95.9%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
244 1
0.4%
243 1
0.4%
242 1
0.4%
241 1
0.4%
240 1
0.4%
239 1
0.4%
238 1
0.4%
237 1
0.4%
236 1
0.4%
235 1
0.4%

지자체
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
강원도
77 
전라남도
47 
충청남도
32 
경상남도
26 
경상북도
24 
Other values (7)
38 

Length

Max length9
Median length4
Mean length3.9098361
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row강원도
2nd row강원도
3rd row강원도
4th row 강원도
5th row강원도

Common Values

ValueCountFrequency (%)
강원도 77
31.6%
전라남도 47
19.3%
충청남도 32
13.1%
경상남도 26
 
10.7%
경상북도 24
 
9.8%
제주특별자치도 12
 
4.9%
전라북도 8
 
3.3%
부산시 7
 
2.9%
강원도 5
 
2.0%
인천시 3
 
1.2%
Other values (2) 3
 
1.2%

Length

2023-12-13T03:28:33.260667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강원도 82
33.6%
전라남도 47
19.3%
충청남도 33
13.5%
경상남도 26
 
10.7%
경상북도 24
 
9.8%
제주특별자치도 12
 
4.9%
전라북도 8
 
3.3%
부산시 7
 
2.9%
인천시 3
 
1.2%
울산시 2
 
0.8%

관리청
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
고성군
28 
태안군
27 
양양군
20 
거제시
16 
강릉시
15 
Other values (42)
138 

Length

Max length6
Median length3
Mean length3.0901639
Min length2

Unique

Unique18 ?
Unique (%)7.4%

Sample

1st row강릉시
2nd row강릉시
3rd row강릉시
4th row 강릉시
5th row강릉시

Common Values

ValueCountFrequency (%)
고성군 28
 
11.5%
태안군 27
 
11.1%
양양군 20
 
8.2%
거제시 16
 
6.6%
강릉시 15
 
6.1%
고흥군 11
 
4.5%
완도군 9
 
3.7%
신안군 9
 
3.7%
여수시 8
 
3.3%
제주시 8
 
3.3%
Other values (37) 93
38.1%

Length

2023-12-13T03:28:33.375206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고성군 28
 
11.5%
태안군 28
 
11.5%
양양군 21
 
8.6%
거제시 16
 
6.6%
강릉시 16
 
6.6%
고흥군 11
 
4.5%
삼척시 10
 
4.1%
완도군 9
 
3.7%
신안군 9
 
3.7%
여수시 8
 
3.3%
Other values (32) 88
36.1%

해수욕장명
Text

Distinct240
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-13T03:28:33.678340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length2.9631148
Min length2

Characters and Unicode

Total characters723
Distinct characters217
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

Unique236 ?
Unique (%)96.7%

Sample

1st row강문
2nd row경포대
3rd row금진
4th row 등명
5th row사근진
ValueCountFrequency (%)
보길 3
 
1.2%
원평 2
 
0.8%
동호 2
 
0.8%
송정 2
 
0.8%
옥계 2
 
0.8%
화진포 2
 
0.8%
청산 2
 
0.8%
시목 1
 
0.4%
배낭기미 1
 
0.4%
백길 1
 
0.4%
Other values (237) 237
92.9%
2023-12-13T03:28:34.401283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
포 39
 
5.4%
33
 
4.6%
리 31
 
4.3%
진 25
 
3.5%
도 18
 
2.5%
대 18
 
2.5%
사 14
 
1.9%
산 13
 
1.8%
지 12
 
1.7%
정 11
 
1.5%
Other values (207) 509
70.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 673
93.1%
Space Separator 33
 
4.6%
Decimal Number 11
 
1.5%
Open Punctuation 3
 
0.4%
Close Punctuation 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
포 39
 
5.8%
리 31
 
4.6%
진 25
 
3.7%
도 18
 
2.7%
대 18
 
2.7%
사 14
 
2.1%
산 13
 
1.9%
지 12
 
1.8%
정 11
 
1.6%
장 10
 
1.5%
Other values (200) 482
71.6%
Decimal Number
ValueCountFrequency (%)
1 6
54.5%
2 3
27.3%
5 1
 
9.1%
3 1
 
9.1%
Space Separator
ValueCountFrequency (%)
33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 673
93.1%
Common 50
 
6.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
포 39
 
5.8%
리 31
 
4.6%
진 25
 
3.7%
도 18
 
2.7%
대 18
 
2.7%
사 14
 
2.1%
산 13
 
1.9%
지 12
 
1.8%
정 11
 
1.6%
장 10
 
1.5%
Other values (200) 482
71.6%
Common
ValueCountFrequency (%)
33
66.0%
1 6
 
12.0%
( 3
 
6.0%
2 3
 
6.0%
) 3
 
6.0%
5 1
 
2.0%
3 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 673
93.1%
ASCII 50
 
6.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
포 39
 
5.8%
리 31
 
4.6%
진 25
 
3.7%
도 18
 
2.7%
대 18
 
2.7%
사 14
 
2.1%
산 13
 
1.9%
지 12
 
1.8%
정 11
 
1.6%
장 10
 
1.5%
Other values (200) 482
71.6%
ASCII
ValueCountFrequency (%)
33
66.0%
1 6
 
12.0%
( 3
 
6.0%
2 3
 
6.0%
) 3
 
6.0%
5 1
 
2.0%
3 1
 
2.0%

개장일
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
07-16
71 
07-03
46 
07-09
45 
07-10
34 
07-01
22 
Other values (5)
26 

Length

Max length5
Median length5
Mean length4.9918033
Min length4

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row07-16
2nd row07-16
3rd row07-16
4th row07-16
5th row07-16

Common Values

ValueCountFrequency (%)
07-16 71
29.1%
07-03 46
18.9%
07-09 45
18.4%
07-10 34
13.9%
07-01 22
 
9.0%
07-15 10
 
4.1%
07-23 9
 
3.7%
07-14 4
 
1.6%
6-01 2
 
0.8%
07-17 1
 
0.4%

Length

2023-12-13T03:28:34.546256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:28:34.660836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
07-16 71
29.1%
07-03 46
18.9%
07-09 45
18.4%
07-10 34
13.9%
07-01 22
 
9.0%
07-15 10
 
4.1%
07-23 9
 
3.7%
07-14 4
 
1.6%
6-01 2
 
0.8%
07-17 1
 
0.4%

폐장일
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
08-22
124 
08-15
46 
08-31
23 
08-29
22 
08-23
18 
Other values (5)
 
11

Length

Max length6
Median length5
Mean length4.9959016
Min length4

Unique

Unique3 ?
Unique (%)1.2%

Sample

1st row08-29
2nd row08-29
3rd row08-29
4th row08-29
5th row08-29

Common Values

ValueCountFrequency (%)
08-22 124
50.8%
08-15 46
 
18.9%
08-31 23
 
9.4%
08-29 22
 
9.0%
08-23 18
 
7.4%
08-16 6
 
2.5%
08-8 2
 
0.8%
08-031 1
 
0.4%
08-17 1
 
0.4%
08-20 1
 
0.4%

Length

2023-12-13T03:28:34.808470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:28:34.933298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
08-22 124
50.8%
08-15 46
 
18.9%
08-31 23
 
9.4%
08-29 22
 
9.0%
08-23 18
 
7.4%
08-16 6
 
2.5%
08-8 2
 
0.8%
08-031 1
 
0.4%
08-17 1
 
0.4%
08-20 1
 
0.4%

이용객수
Real number (ℝ)

HIGH CORRELATION 

Distinct243
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93156.943
Minimum420
Maximum5040678
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-13T03:28:35.108325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum420
5-th percentile1560.1
Q16780.75
median15098.5
Q339545
95-th percentile292426.55
Maximum5040678
Range5040258
Interquartile range (IQR)32764.25

Descriptive statistics

Standard deviation398972.94
Coefficient of variation (CV)4.2828041
Kurtosis106.7853
Mean93156.943
Median Absolute Deviation (MAD)11027.5
Skewness9.5784418
Sum22730294
Variance1.5917941 × 1011
MonotonicityNot monotonic
2023-12-13T03:28:35.266688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9200 2
 
0.8%
58344 1
 
0.4%
19651 1
 
0.4%
37744 1
 
0.4%
1160 1
 
0.4%
17348 1
 
0.4%
17118 1
 
0.4%
2284 1
 
0.4%
11367 1
 
0.4%
878 1
 
0.4%
Other values (233) 233
95.5%
ValueCountFrequency (%)
420 1
0.4%
663 1
0.4%
723 1
0.4%
778 1
0.4%
878 1
0.4%
1063 1
0.4%
1105 1
0.4%
1160 1
0.4%
1181 1
0.4%
1269 1
0.4%
ValueCountFrequency (%)
5040678 1
0.4%
2811030 1
0.4%
1276193 1
0.4%
1230014 1
0.4%
1185000 1
0.4%
1051410 1
0.4%
439936 1
0.4%
427950 1
0.4%
425213 1
0.4%
424370 1
0.4%

Interactions

2023-12-13T03:28:32.635564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:28:32.495765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:28:32.709180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:28:32.565834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:28:35.382403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번지자체관리청개장일폐장일이용객수
연번1.0000.8850.9850.9120.8610.329
지자체0.8851.0001.0000.8100.8190.770
관리청0.9851.0001.0000.9960.9770.920
개장일0.9120.8100.9961.0000.9060.664
폐장일0.8610.8190.9770.9061.0000.479
이용객수0.3290.7700.9200.6640.4791.000
2023-12-13T03:28:35.502170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
폐장일개장일관리청지자체
폐장일1.0000.5100.7650.524
개장일0.5101.0000.8780.511
관리청0.7650.8781.0000.921
지자체0.5240.5110.9211.000
2023-12-13T03:28:35.612790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번이용객수지자체관리청개장일폐장일
연번1.000-0.0550.6390.8050.5240.436
이용객수-0.0551.0000.4500.6640.4590.302
지자체0.6390.4501.0000.9210.5110.524
관리청0.8050.6640.9211.0000.8780.765
개장일0.5240.4590.5110.8781.0000.510
폐장일0.4360.3020.5240.7650.5101.000

Missing values

2023-12-13T03:28:32.811604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:28:32.924544image/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

연번지자체관리청해수욕장명개장일폐장일이용객수
01강원도강릉시강문07-1608-2958344
12강원도강릉시경포대07-1608-29266873
23강원도강릉시금진07-1608-292949
34강원도강릉시등명07-1608-297275
45강원도강릉시사근진07-1608-2912102
56강원도강릉시사천07-1608-296871
67강원도강릉시사천진07-1608-2925239
78강원도강릉시송정07-1608-2927355
89강원도강릉시순긋07-1608-298045
910강원도강릉시안목07-1608-2990962
연번지자체관리청해수욕장명개장일폐장일이용객수
234235충청남도태안군신두리07-0308-1532860
235236충청남도태안군안면07-0308-1515065
236237충청남도태안군어은돌07-0308-1520450
237238충청남도태안군연포07-0308-1517828
238239충청남도태안군의항07-0308-157765
239240충청남도태안군장삼포07-0308-159520
240241충청남도태안군천리포07-0308-1524400
241242충청남도태안군청포대07-0308-1520560
242243충청남도태안군파도리07-0308-1510950
243244충청남도태안군학암포07-0308-1521410