Overview

Dataset statistics

Number of variables8
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory71.9 B

Variable types

Numeric3
Categorical2
Text2
DateTime1

Dataset

Description공원을 제외한 통영시 관내 벤치 설치 현황입니다. 위도, 경도, 주소, 벤치 갯수, 설치 일자 등의 자료를 포함합니다.
Author경상남도 통영시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15099917

Alerts

위도 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
구분 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
담당부서 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
명칭 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:37:03.922568
Analysis finished2023-12-11 00:37:04.975368
Duration1.05 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.853789
Minimum34.825454
Maximum34.936689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T09:37:05.024618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.825454
5-th percentile34.829019
Q134.832544
median34.843824
Q334.864046
95-th percentile34.900405
Maximum34.936689
Range0.111235
Interquartile range (IQR)0.031502

Descriptive statistics

Standard deviation0.02796869
Coefficient of variation (CV)0.00080245766
Kurtosis1.5759915
Mean34.853789
Median Absolute Deviation (MAD)0.011656
Skewness1.4108779
Sum941.05231
Variance0.00078224763
MonotonicityNot monotonic
2023-12-11T09:37:05.139454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
34.843701 2
 
7.4%
34.884315 1
 
3.7%
34.843824 1
 
3.7%
34.836242 1
 
3.7%
34.839508 1
 
3.7%
34.936689 1
 
3.7%
34.825454 1
 
3.7%
34.832168 1
 
3.7%
34.831265 1
 
3.7%
34.83292 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
34.825454 1
3.7%
34.829018 1
3.7%
34.82902 1
3.7%
34.82903 1
3.7%
34.829067 1
3.7%
34.831265 1
3.7%
34.832168 1
3.7%
34.83292 1
3.7%
34.836242 1
3.7%
34.839508 1
3.7%
ValueCountFrequency (%)
34.936689 1
3.7%
34.901456 1
3.7%
34.897951 1
3.7%
34.889046 1
3.7%
34.884315 1
3.7%
34.883863 1
3.7%
34.873392 1
3.7%
34.8547 1
3.7%
34.8529 1
3.7%
34.852 1
3.7%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.41624
Minimum128.35191
Maximum128.4315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T09:37:05.256063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.35191
5-th percentile128.39749
Q1128.41609
median128.41976
Q3128.42193
95-th percentile128.43077
Maximum128.4315
Range0.079593
Interquartile range (IQR)0.005843

Descriptive statistics

Standard deviation0.014910799
Coefficient of variation (CV)0.00011611303
Kurtosis13.807866
Mean128.41624
Median Absolute Deviation (MAD)0.002406
Skewness-3.3512986
Sum3467.2384
Variance0.00022233192
MonotonicityNot monotonic
2023-12-11T09:37:05.377248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
128.41989 2
 
7.4%
128.423021 1
 
3.7%
128.420081 1
 
3.7%
128.411476 1
 
3.7%
128.41461 1
 
3.7%
128.351907 1
 
3.7%
128.416733 1
 
3.7%
128.419043 1
 
3.7%
128.418754 1
 
3.7%
128.420507 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
128.351907 1
3.7%
128.394107 1
3.7%
128.405391 1
3.7%
128.409528 1
3.7%
128.411476 1
3.7%
128.41461 1
3.7%
128.415444 1
3.7%
128.416733 1
3.7%
128.4185 1
3.7%
128.418576 1
3.7%
ValueCountFrequency (%)
128.4315 1
3.7%
128.4308 1
3.7%
128.4307 1
3.7%
128.423021 1
3.7%
128.422389 1
3.7%
128.422161 1
3.7%
128.422069 1
3.7%
128.421794 1
3.7%
128.420975 1
3.7%
128.420507 1
3.7%

구분
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Memory size348.0 B
주택 인근
5 
공원 인근
4 
주거지인근쉼터
3 
주거지 인근 쉼터
3 
도로
2 
Other values (7)
10 

Length

Max length9
Median length7
Mean length4.962963
Min length2

Unique

Unique4 ?
Unique (%)14.8%

Sample

1st row도로
2nd row마을
3rd row도로
4th row마을
5th row하천

Common Values

ValueCountFrequency (%)
주택 인근 5
18.5%
공원 인근 4
14.8%
주거지인근쉼터 3
11.1%
주거지 인근 쉼터 3
11.1%
도로 2
 
7.4%
마을 2
 
7.4%
하천 2
 
7.4%
아파트 인근 2
 
7.4%
원룸 인근 1
 
3.7%
쉼터 1
 
3.7%
Other values (2) 2
 
7.4%

Length

2023-12-11T09:37:05.485525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
인근 15
33.3%
주택 5
 
11.1%
공원 4
 
8.9%
쉼터 4
 
8.9%
주거지인근쉼터 3
 
6.7%
주거지 3
 
6.7%
도로 2
 
4.4%
마을 2
 
4.4%
하천 2
 
4.4%
아파트 2
 
4.4%
Other values (3) 3
 
6.7%

명칭
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-11T09:37:05.648212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length8.2592593
Min length2

Characters and Unicode

Total characters223
Distinct characters87
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 row죽림신시가지 해안도로변
2nd row손덕마을
3rd row죽림 산책로
4th row용호마을
5th row죽림천
ValueCountFrequency (%)
인근 5
 
9.4%
데메3길 4
 
7.5%
쉼터 2
 
3.8%
백동햇님마을아파트 2
 
3.8%
명정동 2
 
3.8%
서피랑공원 2
 
3.8%
주택가-3 1
 
1.9%
서피랑터널 1
 
1.9%
장공장 1
 
1.9%
옆 1
 
1.9%
Other values (32) 32
60.4%
2023-12-11T09:37:05.921118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26
 
11.7%
인 8
 
3.6%
근 7
 
3.1%
터 7
 
3.1%
길 6
 
2.7%
- 6
 
2.7%
가 6
 
2.7%
원 5
 
2.2%
3 5
 
2.2%
쉼 5
 
2.2%
Other values (77) 142
63.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 181
81.2%
Space Separator 26
 
11.7%
Decimal Number 10
 
4.5%
Dash Punctuation 6
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 8
 
4.4%
근 7
 
3.9%
터 7
 
3.9%
길 6
 
3.3%
가 6
 
3.3%
원 5
 
2.8%
쉼 5
 
2.8%
피 5
 
2.8%
서 5
 
2.8%
마 4
 
2.2%
Other values (71) 123
68.0%
Decimal Number
ValueCountFrequency (%)
3 5
50.0%
1 2
 
20.0%
2 2
 
20.0%
4 1
 
10.0%
Space Separator
ValueCountFrequency (%)
26
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 181
81.2%
Common 42
 
18.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 8
 
4.4%
근 7
 
3.9%
터 7
 
3.9%
길 6
 
3.3%
가 6
 
3.3%
원 5
 
2.8%
쉼 5
 
2.8%
피 5
 
2.8%
서 5
 
2.8%
마 4
 
2.2%
Other values (71) 123
68.0%
Common
ValueCountFrequency (%)
26
61.9%
- 6
 
14.3%
3 5
 
11.9%
1 2
 
4.8%
2 2
 
4.8%
4 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 181
81.2%
ASCII 42
 
18.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26
61.9%
- 6
 
14.3%
3 5
 
11.9%
1 2
 
4.8%
2 2
 
4.8%
4 1
 
2.4%
Hangul
ValueCountFrequency (%)
인 8
 
4.4%
근 7
 
3.9%
터 7
 
3.9%
길 6
 
3.3%
가 6
 
3.3%
원 5
 
2.8%
쉼 5
 
2.8%
피 5
 
2.8%
서 5
 
2.8%
마 4
 
2.2%
Other values (71) 123
68.0%

주소
Text

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-11T09:37:06.083080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length18.740741
Min length15

Characters and Unicode

Total characters506
Distinct characters44
Distinct categories6 ?
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 (%)92.6%

Sample

1st row경상남도 통영시 광도면 죽림리 1590
2nd row경상남도 통영시 광도면 덕포리 967-6
3rd row경상남도 통영시 광도면 죽림리 1586
4th row경상남도 통영시 광도면 용호리 247
5th row경상남도 통영시 광도면 죽림리 1521-7
ValueCountFrequency (%)
경상남도 27
23.5%
통영시 27
23.5%
광도면 6
 
5.2%
서호동 5
 
4.3%
봉평동 4
 
3.5%
도남동 4
 
3.5%
죽림리 3
 
2.6%
북신동 3
 
2.6%
141-1 2
 
1.7%
명정동 2
 
1.7%
Other values (32) 32
27.8%
2023-12-11T09:37:06.348262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
88
17.4%
도 40
 
7.9%
남 31
 
6.1%
1 29
 
5.7%
경 27
 
5.3%
통 27
 
5.3%
영 27
 
5.3%
시 27
 
5.3%
상 27
 
5.3%
- 22
 
4.3%
Other values (34) 161
31.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 293
57.9%
Decimal Number 101
 
20.0%
Space Separator 88
 
17.4%
Dash Punctuation 22
 
4.3%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 40
13.7%
남 31
10.6%
경 27
9.2%
통 27
9.2%
영 27
9.2%
시 27
9.2%
상 27
9.2%
동 20
 
6.8%
면 7
 
2.4%
리 7
 
2.4%
Other values (20) 53
18.1%
Decimal Number
ValueCountFrequency (%)
1 29
28.7%
2 12
11.9%
8 10
 
9.9%
5 9
 
8.9%
6 9
 
8.9%
7 9
 
8.9%
4 7
 
6.9%
3 7
 
6.9%
9 6
 
5.9%
0 3
 
3.0%
Space Separator
ValueCountFrequency (%)
88
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 293
57.9%
Common 213
42.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 40
13.7%
남 31
10.6%
경 27
9.2%
통 27
9.2%
영 27
9.2%
시 27
9.2%
상 27
9.2%
동 20
 
6.8%
면 7
 
2.4%
리 7
 
2.4%
Other values (20) 53
18.1%
Common
ValueCountFrequency (%)
88
41.3%
1 29
 
13.6%
- 22
 
10.3%
2 12
 
5.6%
8 10
 
4.7%
5 9
 
4.2%
6 9
 
4.2%
7 9
 
4.2%
4 7
 
3.3%
3 7
 
3.3%
Other values (4) 11
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 293
57.9%
ASCII 213
42.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
88
41.3%
1 29
 
13.6%
- 22
 
10.3%
2 12
 
5.6%
8 10
 
4.7%
5 9
 
4.2%
6 9
 
4.2%
7 9
 
4.2%
4 7
 
3.3%
3 7
 
3.3%
Other values (4) 11
 
5.2%
Hangul
ValueCountFrequency (%)
도 40
13.7%
남 31
10.6%
경 27
9.2%
통 27
9.2%
영 27
9.2%
시 27
9.2%
상 27
9.2%
동 20
 
6.8%
면 7
 
2.4%
리 7
 
2.4%
Other values (20) 53
18.1%

개수
Real number (ℝ)

Distinct8
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4444444
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T09:37:06.446151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.5
median2
Q34
95-th percentile9.7
Maximum10
Range9
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.7080128
Coefficient of variation (CV)0.78619726
Kurtosis1.1423649
Mean3.4444444
Median Absolute Deviation (MAD)1
Skewness1.3583619
Sum93
Variance7.3333333
MonotonicityNot monotonic
2023-12-11T09:37:06.529816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 7
25.9%
1 7
25.9%
4 5
18.5%
10 2
 
7.4%
3 2
 
7.4%
6 2
 
7.4%
9 1
 
3.7%
5 1
 
3.7%
ValueCountFrequency (%)
1 7
25.9%
2 7
25.9%
3 2
 
7.4%
4 5
18.5%
5 1
 
3.7%
6 2
 
7.4%
9 1
 
3.7%
10 2
 
7.4%
ValueCountFrequency (%)
10 2
 
7.4%
9 1
 
3.7%
6 2
 
7.4%
5 1
 
3.7%
4 5
18.5%
3 2
 
7.4%
2 7
25.9%
1 7
25.9%

담당부서
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size348.0 B
봉평동
8 
명정동
7 
광도면
6 
북신동
3 
도천동
2 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)3.7%

Sample

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

Common Values

ValueCountFrequency (%)
봉평동 8
29.6%
명정동 7
25.9%
광도면 6
22.2%
북신동 3
 
11.1%
도천동 2
 
7.4%
도산면 1
 
3.7%

Length

2023-12-11T09:37:06.619405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:37:06.698928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
봉평동 8
29.6%
명정동 7
25.9%
광도면 6
22.2%
북신동 3
 
11.1%
도천동 2
 
7.4%
도산면 1
 
3.7%

설치 일자
Date

Distinct17
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
Minimum2009-10-21 00:00:00
Maximum2022-02-14 00:00:00
2023-12-11T09:37:06.785975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:06.874445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)

Interactions

2023-12-11T09:37:04.624190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:04.210689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:04.408129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:04.687439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:04.275695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:04.478289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:04.753459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:04.347444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:04.557571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:37:06.950135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도구분명칭주소개수담당부서설치 일자
위도1.0000.8350.9351.0001.0000.3770.9240.949
경도0.8351.0000.8441.0001.0000.5550.9080.943
구분0.9350.8441.0001.0001.0000.6121.0000.920
명칭1.0001.0001.0001.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.0000.0001.0001.000
개수0.3770.5550.6121.0000.0001.0000.0000.861
담당부서0.9240.9081.0001.0001.0000.0001.0001.000
설치 일자0.9490.9430.9201.0001.0000.8611.0001.000
2023-12-11T09:37:07.031216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
담당부서구분
담당부서1.0000.845
구분0.8451.000
2023-12-11T09:37:07.096407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도개수구분담당부서
위도1.000-0.2010.4460.7050.822
경도-0.2011.0000.0480.6140.670
개수0.4460.0481.0000.2400.000
구분0.7050.6140.2401.0000.845
담당부서0.8220.6700.0000.8451.000

Missing values

2023-12-11T09:37:04.833355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:37:04.938597image/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

위도경도구분명칭주소개수담당부서설치 일자
034.884315128.423021도로죽림신시가지 해안도로변경상남도 통영시 광도면 죽림리 159010광도면2009-10-21
134.901456128.420975마을손덕마을경상남도 통영시 광도면 덕포리 967-610광도면2016-05-16
234.889046128.418749도로죽림 산책로경상남도 통영시 광도면 죽림리 15863광도면2019-04-23
334.873392128.394107마을용호마을경상남도 통영시 광도면 용호리 2474광도면2019-04-23
434.883863128.409528하천죽림천경상남도 통영시 광도면 죽림리 1521-76광도면2020-03-03
534.897951128.405391하천광도천경상남도 통영시 광도면 노산리 461-29광도면2020-03-12
634.8547128.4307주거지인근쉼터해미당쉼터경상남도 통영시 북신동 56-1354북신동2022-02-14
734.8529128.4308주거지인근쉼터튼튼어린이집 앞 쉼터경상남도 통영시 북신동 59-152북신동2022-02-14
834.852128.4315주거지인근쉼터장대사거리쉼터경상남도 통영시 북신동 75-12북신동2022-02-14
934.84641128.415444주거지 인근 쉼터명정동 노인회관 인근경상남도 통영시 명정동 472-82명정동2015-12-28
위도경도구분명칭주소개수담당부서설치 일자
1734.829067128.422161주택 인근데메3길 주택가-2경상남도 통영시 도남동 382-21봉평동2021-04-20
1834.829018128.422069주택 인근데메3길 주택가-3경상남도 통영시 도남동 382-41봉평동2021-04-20
1934.82903128.422389주택 인근데메3길 주택가-4경상남도 통영시 도남동 386-71봉평동2021-04-20
2034.83292128.420507원룸 인근서송정길 원룸촌경상남도 통영시 봉평동 3-16봉평동2020-12-25
2134.831265128.418754아파트 인근백동햇님마을아파트 인근-1경상남도 통영시 봉평동 86-14봉평동2020-12-25
2234.832168128.419043아파트 인근백동햇님마을아파트 인근-2경상남도 통영시 봉평동 12-14봉평동2020-12-25
2334.825454128.416733주택 인근봉수골 한평정원경상남도 통영시 봉평동 218-12봉평동2022-02-03
2434.936689128.351907쉼터쉼터경상남도 통영시 도산면 원산리 1019-61도산면2015-10-29
2534.839508128.41461주거지인근벽산쉼터경상남도 통영시 도천동 248-11도천동2016-12-21
2634.836242128.411476역사유적지해저터널경상남도 통영시 도천1길 7(당동)1도천동2016-12-21