Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 KiB
Average record size in memory42.3 B

Variable types

Categorical4
Text1

Dataset

DescriptionSample
Author㈜지오시스템리서치
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT09GSR009

Alerts

YR has constant value ""Constant
SIDO_NM is highly overall correlated with SGG_NMHigh correlation
SGG_NM is highly overall correlated with SIDO_NMHigh correlation
TRGET_AREA_NM has unique valuesUnique

Reproduction

Analysis started2024-03-13 12:41:22.656186
Analysis finished2024-03-13 12:41:23.161403
Duration0.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SIDO_NM
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충남
31 
전남
25 
인천
14 
전북
11 
부산
9 
Other values (2)
10 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충남 31
31.0%
전남 25
25.0%
인천 14
14.0%
전북 11
 
11.0%
부산 9
 
9.0%
울산 5
 
5.0%
경기 5
 
5.0%

Length

2024-03-13T21:41:23.252585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:41:23.396293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충남 31
31.0%
전남 25
25.0%
인천 14
14.0%
전북 11
 
11.0%
부산 9
 
9.0%
울산 5
 
5.0%
경기 5
 
5.0%

SGG_NM
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
태안군
13 
무안군
12 
옹진군
8 
신안군
6 
영광군
 
5
Other values (21)
56 

Length

Max length4
Median length3
Mean length2.93
Min length2

Unique

Unique6 ?
Unique (%)6.0%

Sample

1st row기장군
2nd row기장군
3rd row해운대구
4th row해운대구
5th row수영구

Common Values

ValueCountFrequency (%)
태안군 13
13.0%
무안군 12
 
12.0%
옹진군 8
 
8.0%
신안군 6
 
6.0%
영광군 5
 
5.0%
부안군 5
 
5.0%
서천군 5
 
5.0%
보령시 5
 
5.0%
중구 5
 
5.0%
안산시 5
 
5.0%
Other values (16) 31
31.0%

Length

2024-03-13T21:41:23.600916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
태안군 13
13.0%
무안군 12
 
12.0%
옹진군 8
 
8.0%
신안군 6
 
6.0%
영광군 5
 
5.0%
부안군 5
 
5.0%
서천군 5
 
5.0%
보령시 5
 
5.0%
중구 5
 
5.0%
안산시 5
 
5.0%
Other values (16) 31
31.0%

TRGET_AREA_NM
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-03-13T21:41:23.979714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length8.91
Min length2

Characters and Unicode

Total characters891
Distinct characters139
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

Unique100 ?
Unique (%)100.0%

Sample

1st row기장군 임랑해수욕장
2nd row기장군 일광해수욕장
3rd row해운대구 송정해수욕장
4th row해운대구 해운대해수욕장
5th row수영구 광안리해수욕장
ValueCountFrequency (%)
태안군 13
 
6.6%
옹진군 8
 
4.0%
무안군 6
 
3.0%
신안군 6
 
3.0%
서천군 5
 
2.5%
영광군 5
 
2.5%
홍성군 5
 
2.5%
보령시 5
 
2.5%
중구 5
 
2.5%
부안군 5
 
2.5%
Other values (121) 135
68.2%
2024-03-13T21:41:24.567600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
98
 
11.0%
군 66
 
7.4%
해 64
 
7.2%
장 59
 
6.6%
수 52
 
5.8%
욕 51
 
5.7%
안 43
 
4.8%
구 33
 
3.7%
지 19
 
2.1%
리 19
 
2.1%
Other values (129) 387
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 789
88.6%
Space Separator 98
 
11.0%
Math Symbol 2
 
0.2%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 66
 
8.4%
해 64
 
8.1%
장 59
 
7.5%
수 52
 
6.6%
욕 51
 
6.5%
안 43
 
5.4%
구 33
 
4.2%
지 19
 
2.4%
리 19
 
2.4%
시 16
 
2.0%
Other values (124) 367
46.5%
Math Symbol
ValueCountFrequency (%)
~ 1
50.0%
∼ 1
50.0%
Space Separator
ValueCountFrequency (%)
98
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 789
88.6%
Common 102
 
11.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 66
 
8.4%
해 64
 
8.1%
장 59
 
7.5%
수 52
 
6.6%
욕 51
 
6.5%
안 43
 
5.4%
구 33
 
4.2%
지 19
 
2.4%
리 19
 
2.4%
시 16
 
2.0%
Other values (124) 367
46.5%
Common
ValueCountFrequency (%)
98
96.1%
~ 1
 
1.0%
) 1
 
1.0%
∼ 1
 
1.0%
( 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 789
88.6%
ASCII 101
 
11.3%
Math Operators 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
98
97.0%
~ 1
 
1.0%
) 1
 
1.0%
( 1
 
1.0%
Hangul
ValueCountFrequency (%)
군 66
 
8.4%
해 64
 
8.1%
장 59
 
7.5%
수 52
 
6.6%
욕 51
 
6.5%
안 43
 
5.4%
구 33
 
4.2%
지 19
 
2.4%
리 19
 
2.4%
시 16
 
2.0%
Other values (124) 367
46.5%
Math Operators
ValueCountFrequency (%)
∼ 1
100.0%

YR
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2021
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 100
100.0%

Length

2024-03-13T21:41:24.744995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:41:24.894111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 100
100.0%

WASH_GRD_CD
Categorical

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
B
68 
C
25 
D
 
5
A
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowC
3rd rowC
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B 68
68.0%
C 25
 
25.0%
D 5
 
5.0%
A 2
 
2.0%

Length

2024-03-13T21:41:25.031735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:41:25.175615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 68
68.0%
c 25
 
25.0%
d 5
 
5.0%
a 2
 
2.0%

Correlations

2024-03-13T21:41:25.269636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIDO_NMSGG_NMTRGET_AREA_NMWASH_GRD_CD
SIDO_NM1.0001.0001.0000.249
SGG_NM1.0001.0001.0000.000
TRGET_AREA_NM1.0001.0001.0001.000
WASH_GRD_CD0.2490.0001.0001.000
2024-03-13T21:41:25.409905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WASH_GRD_CDSIDO_NMSGG_NM
WASH_GRD_CD1.0000.1690.000
SIDO_NM0.1691.0000.892
SGG_NM0.0000.8921.000
2024-03-13T21:41:25.538818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIDO_NMSGG_NMWASH_GRD_CD
SIDO_NM1.0000.8920.169
SGG_NM0.8921.0000.000
WASH_GRD_CD0.1690.0001.000

Missing values

2024-03-13T21:41:22.971062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:41:23.109370image/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

SIDO_NMSGG_NMTRGET_AREA_NMYRWASH_GRD_CD
0부산기장군기장군 임랑해수욕장2021B
1부산기장군기장군 일광해수욕장2021C
2부산해운대구해운대구 송정해수욕장2021C
3부산해운대구해운대구 해운대해수욕장2021B
4부산수영구수영구 광안리해수욕장2021B
5부산영도구영도구 중리해변2021B
6부산영도구영도구 감지해변2021B
7부산서구서구 송도해수욕장2021B
8부산사하구사하구 다대포해안 동측지구2021C
9울산북구북구 정자해수욕장2021B
SIDO_NMSGG_NMTRGET_AREA_NMYRWASH_GRD_CD
90전남무안군신월2021C
91전남무안군창매2021C
92전남무안군해운2021B
93전남무안군현화2021B
94전남신안군신안군 대광해수욕장2021B
95전남신안군신안군 우전해수욕장2021B
96전남신안군신안군 둔장해수욕장2021C
97전남신안군신안군 짝지지구2021B
98전남신안군신안군 남촌지구2021D
99전남신안군신안군 익금리2021B