Overview

Dataset statistics

Number of variables7
Number of observations1939
Missing cells0
Missing cells (%)0.0%
Duplicate rows3
Duplicate rows (%)0.2%
Total size in memory108.1 KiB
Average record size in memory57.1 B

Variable types

Text3
Numeric1
Categorical3

Dataset

Description경남 각시군별 위치한 인공어초에 관한 시설위치, 지역명, 년도, 종류, 면적, 사업비등과 같은 정보를 제공합니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3034247

Alerts

Dataset has 3 (0.2%) duplicate rowsDuplicates
시설년도 is highly overall correlated with 어초종류High correlation
어초종류 is highly overall correlated with 시설년도 and 1 other fieldsHigh correlation
확인좌표(시)(위도E) is highly overall correlated with 확인좌표(시)(경도N)High correlation
확인좌표(시)(경도N) is highly overall correlated with 어초종류 and 1 other fieldsHigh correlation
확인좌표(시)(위도E) is highly imbalanced (80.2%)Imbalance
확인좌표(시)(경도N) is highly imbalanced (60.7%)Imbalance

Reproduction

Analysis started2023-12-10 23:53:30.475924
Analysis finished2023-12-10 23:53:31.092629
Duration0.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역명
Text

Distinct422
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
2023-12-11T08:53:31.356267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length9.1052089
Min length5

Characters and Unicode

Total characters17655
Distinct characters200
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

Unique191 ?
Unique (%)9.9%

Sample

1st row통영 한산 매물
2nd row통영 욕지 동항
3rd row통영 욕지 동항
4th row통영 욕지 동항
5th row통영 욕지 동항
ValueCountFrequency (%)
통영 988
 
16.0%
욕지 411
 
6.6%
남해 409
 
6.6%
거제 360
 
5.8%
한산 311
 
5.0%
미조 228
 
3.7%
남부 180
 
2.9%
산양 177
 
2.9%
연화 130
 
2.1%
상주 114
 
1.8%
Other values (408) 2878
46.5%
2023-12-11T08:53:32.077097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4385
24.8%
영 1004
 
5.7%
통 1003
 
5.7%
남 716
 
4.1%
도 715
 
4.0%
산 587
 
3.3%
지 509
 
2.9%
해 505
 
2.9%
욕 419
 
2.4%
거 381
 
2.2%
Other values (190) 7431
42.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13237
75.0%
Space Separator 4385
 
24.8%
Decimal Number 31
 
0.2%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 1004
 
7.6%
통 1003
 
7.6%
남 716
 
5.4%
도 715
 
5.4%
산 587
 
4.4%
지 509
 
3.8%
해 505
 
3.8%
욕 419
 
3.2%
거 381
 
2.9%
제 375
 
2.8%
Other values (185) 7023
53.1%
Decimal Number
ValueCountFrequency (%)
2 18
58.1%
1 13
41.9%
Space Separator
ValueCountFrequency (%)
4385
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13237
75.0%
Common 4418
 
25.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 1004
 
7.6%
통 1003
 
7.6%
남 716
 
5.4%
도 715
 
5.4%
산 587
 
4.4%
지 509
 
3.8%
해 505
 
3.8%
욕 419
 
3.2%
거 381
 
2.9%
제 375
 
2.8%
Other values (185) 7023
53.1%
Common
ValueCountFrequency (%)
4385
99.3%
2 18
 
0.4%
1 13
 
0.3%
( 1
 
< 0.1%
) 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13237
75.0%
ASCII 4418
 
25.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4385
99.3%
2 18
 
0.4%
1 13
 
0.3%
( 1
 
< 0.1%
) 1
 
< 0.1%
Hangul
ValueCountFrequency (%)
영 1004
 
7.6%
통 1003
 
7.6%
남 716
 
5.4%
도 715
 
5.4%
산 587
 
4.4%
지 509
 
3.8%
해 505
 
3.8%
욕 419
 
3.2%
거 381
 
2.9%
제 375
 
2.8%
Other values (185) 7023
53.1%

시설년도
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1996.9567
Minimum1972
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2023-12-11T08:53:32.226829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1972
5-th percentile1982
Q11989.5
median1996
Q32003
95-th percentile2014
Maximum2017
Range45
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation9.9617007
Coefficient of variation (CV)0.0049884411
Kurtosis-0.70861663
Mean1996.9567
Median Absolute Deviation (MAD)7
Skewness0.18082926
Sum3872099
Variance99.23548
MonotonicityIncreasing
2023-12-11T08:53:32.384265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1993 96
 
5.0%
1986 92
 
4.7%
1997 90
 
4.6%
1996 86
 
4.4%
2012 85
 
4.4%
2000 83
 
4.3%
2002 79
 
4.1%
1998 78
 
4.0%
1995 73
 
3.8%
1994 72
 
3.7%
Other values (31) 1105
57.0%
ValueCountFrequency (%)
1972 13
 
0.7%
1976 4
 
0.2%
1978 2
 
0.1%
1979 4
 
0.2%
1980 6
 
0.3%
1981 15
 
0.8%
1982 60
3.1%
1983 61
3.1%
1984 62
3.2%
1985 49
2.5%
ValueCountFrequency (%)
2017 9
 
0.5%
2016 40
2.1%
2015 43
2.2%
2014 41
2.1%
2013 53
2.7%
2012 85
4.4%
2011 32
 
1.7%
2010 13
 
0.7%
2008 5
 
0.3%
2007 16
 
0.8%

어초종류
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
사각형어초
937 
사각+반구형어초
203 
반구형어초
160 
잠보형어초
 
77
팔각상자형강제어초
 
60
Other values (42)
502 

Length

Max length14
Median length5
Mean length6.1784425
Min length4

Unique

Unique8 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
사각형어초 937
48.3%
사각+반구형어초 203
 
10.5%
반구형어초 160
 
8.3%
잠보형어초 77
 
4.0%
팔각상자형강제어초 60
 
3.1%
팔각반구형소형강제어초 45
 
2.3%
2단상자형강제어초 42
 
2.2%
팔각반구형중형강제어초 37
 
1.9%
강제어선어초 36
 
1.9%
상자형강제어초 34
 
1.8%
Other values (37) 308
 
15.9%

Length

2023-12-11T08:53:32.541111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사각형어초 937
48.3%
사각+반구형어초 203
 
10.5%
반구형어초 160
 
8.3%
잠보형어초 77
 
4.0%
팔각상자형강제어초 60
 
3.1%
팔각반구형소형강제어초 45
 
2.3%
2단상자형강제어초 42
 
2.2%
팔각반구형중형강제어초 37
 
1.9%
강제어선어초 36
 
1.9%
상자형강제어초 34
 
1.8%
Other values (37) 308
 
15.9%

확인좌표(시)(위도E)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
34˚
1795 
34°
 
91
35˚
 
51
34
 
1
35°
 
1

Length

Max length3
Median length3
Mean length2.9994843
Min length2

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row34˚
2nd row34˚
3rd row34˚
4th row34˚
5th row34˚

Common Values

ValueCountFrequency (%)
34˚ 1795
92.6%
34° 91
 
4.7%
35˚ 51
 
2.6%
34 1
 
0.1%
35° 1
 
0.1%

Length

2023-12-11T08:53:32.673947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:53:32.769107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
34˚ 1795
92.6%
34° 91
 
4.7%
35˚ 51
 
2.6%
34 1
 
0.1%
35° 1
 
0.1%

확인좌표(분)(위도E)
Text

Distinct1817
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
2023-12-11T08:53:33.060237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.0041258
Min length7

Characters and Unicode

Total characters13581
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1702 ?
Unique (%)87.8%

Sample

1st row38.113'
2nd row38.029'
3rd row38.346'
4th row38.400'
5th row38.610'
ValueCountFrequency (%)
42.298 3
 
0.2%
41.698 3
 
0.2%
39.308 3
 
0.2%
42.987 3
 
0.2%
39.356 3
 
0.2%
41.489 3
 
0.2%
45.526 3
 
0.2%
41.896 2
 
0.1%
43.525′ 2
 
0.1%
42.381 2
 
0.1%
Other values (1807) 1912
98.6%
2023-12-11T08:53:33.580876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 1942
14.3%
. 1939
14.3%
' 1850
13.6%
3 1300
9.6%
5 1094
8.1%
6 823
6.1%
9 820
6.0%
2 783
5.8%
1 776
 
5.7%
8 767
 
5.6%
Other values (3) 1487
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9703
71.4%
Other Punctuation 3878
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1942
20.0%
3 1300
13.4%
5 1094
11.3%
6 823
8.5%
9 820
8.5%
2 783
8.1%
1 776
 
8.0%
8 767
 
7.9%
7 712
 
7.3%
0 686
 
7.1%
Other Punctuation
ValueCountFrequency (%)
. 1939
50.0%
' 1850
47.7%
′ 89
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 13581
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1942
14.3%
. 1939
14.3%
' 1850
13.6%
3 1300
9.6%
5 1094
8.1%
6 823
6.1%
9 820
6.0%
2 783
5.8%
1 776
 
5.7%
8 767
 
5.6%
Other values (3) 1487
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13492
99.3%
Punctuation 89
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 1942
14.4%
. 1939
14.4%
' 1850
13.7%
3 1300
9.6%
5 1094
8.1%
6 823
6.1%
9 820
6.1%
2 783
5.8%
1 776
 
5.8%
8 767
 
5.7%
Other values (2) 1398
10.4%
Punctuation
ValueCountFrequency (%)
′ 89
100.0%

확인좌표(시)(경도N)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
128˚
1646 
127˚
201 
128°
 
69
127°
 
23

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row128˚
2nd row128˚
3rd row128˚
4th row128˚
5th row128˚

Common Values

ValueCountFrequency (%)
128˚ 1646
84.9%
127˚ 201
 
10.4%
128° 69
 
3.6%
127° 23
 
1.2%

Length

2023-12-11T08:53:33.719321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:53:33.820381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
128˚ 1646
84.9%
127˚ 201
 
10.4%
128° 69
 
3.6%
127° 23
 
1.2%

확인좌표(분)(경도N)
Text

Distinct1878
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
2023-12-11T08:53:34.129139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.0041258
Min length7

Characters and Unicode

Total characters13581
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1820 ?
Unique (%)93.9%

Sample

1st row35.055'
2nd row18.465'
3rd row18.324'
4th row18.867'
5th row18.656'
ValueCountFrequency (%)
22.850 3
 
0.2%
30.407 3
 
0.2%
04.186 3
 
0.2%
19.945 2
 
0.1%
28.648 2
 
0.1%
27.692 2
 
0.1%
22.872 2
 
0.1%
54.624 2
 
0.1%
51.893′ 2
 
0.1%
01.001 2
 
0.1%
Other values (1868) 1916
98.8%
2023-12-11T08:53:34.615610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1939
14.3%
' 1850
13.6%
2 1323
9.7%
1 1213
8.9%
0 1174
8.6%
3 1125
8.3%
4 1048
7.7%
5 847
6.2%
8 818
6.0%
9 786
5.8%
Other values (3) 1458
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9703
71.4%
Other Punctuation 3878
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1323
13.6%
1 1213
12.5%
0 1174
12.1%
3 1125
11.6%
4 1048
10.8%
5 847
8.7%
8 818
8.4%
9 786
8.1%
7 760
7.8%
6 609
6.3%
Other Punctuation
ValueCountFrequency (%)
. 1939
50.0%
' 1850
47.7%
′ 89
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 13581
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1939
14.3%
' 1850
13.6%
2 1323
9.7%
1 1213
8.9%
0 1174
8.6%
3 1125
8.3%
4 1048
7.7%
5 847
6.2%
8 818
6.0%
9 786
5.8%
Other values (3) 1458
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13492
99.3%
Punctuation 89
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1939
14.4%
' 1850
13.7%
2 1323
9.8%
1 1213
9.0%
0 1174
8.7%
3 1125
8.3%
4 1048
7.8%
5 847
6.3%
8 818
6.1%
9 786
5.8%
Other values (2) 1369
10.1%
Punctuation
ValueCountFrequency (%)
′ 89
100.0%

Interactions

2023-12-11T08:53:30.840631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:53:34.722660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설년도어초종류확인좌표(시)(위도E)확인좌표(시)(경도N)
시설년도1.0000.8920.6950.609
어초종류0.8921.0000.7660.845
확인좌표(시)(위도E)0.6950.7661.0000.648
확인좌표(시)(경도N)0.6090.8450.6481.000
2023-12-11T08:53:34.826342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
확인좌표(시)(위도E)확인좌표(시)(경도N)어초종류
확인좌표(시)(위도E)1.0000.5780.474
확인좌표(시)(경도N)0.5781.0000.597
어초종류0.4740.5971.000
2023-12-11T08:53:34.917408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설년도어초종류확인좌표(시)(위도E)확인좌표(시)(경도N)
시설년도1.0000.5740.3590.412
어초종류0.5741.0000.4740.597
확인좌표(시)(위도E)0.3590.4741.0000.578
확인좌표(시)(경도N)0.4120.5970.5781.000

Missing values

2023-12-11T08:53:30.942440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:53:31.046842image/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

지역명시설년도어초종류확인좌표(시)(위도E)확인좌표(분)(위도E)확인좌표(시)(경도N)확인좌표(분)(경도N)
0통영 한산 매물1972소형사각형어초34˚38.113'128˚35.055'
1통영 욕지 동항1972소형사각형어초34˚38.029'128˚18.465'
2통영 욕지 동항1972소형사각형어초34˚38.346'128˚18.324'
3통영 욕지 동항1972소형사각형어초34˚38.400'128˚18.867'
4통영 욕지 동항1972소형사각형어초34˚38.610'128˚18.656'
5통영 욕지 동항1972소형사각형어초34˚38.542'128˚18.724'
6통영 욕지 동항1972소형사각형어초34˚38.573'128˚18.596'
7통영 욕지 동항1972소형사각형어초34˚38.651'128˚18.535'
8통영 욕지 동항1972소형사각형어초34˚38.746'128˚18.465'
9거제 남부 다포1972소형사각형어초34˚42.927'128˚38.891'
지역명시설년도어초종류확인좌표(시)(위도E)확인좌표(분)(위도E)확인좌표(시)(경도N)확인좌표(분)(경도N)
1929남해군 미조면 미조리 답하해역2016신요철형어초34°42.195′128°02.124′
1930남해군 갈화해역2017신요철형어초34°54.525′127°49.649′
1931남해군 작장해역2017테트라형어초34°49.973′127°48.765′
1932남해군 사촌해역2017신요철형어초34°44.508′127°51.190′
1933남해군 향촌해역2017사각형어초34°43.510′127°52.222′
1934통영시 사량면 옥동 해역2017신요철형어초34°50.345′128°12.201′
1935통영시 욕지면 옥동산내 해역2017팔각별강제인공어초34°37.193′128°17.821′
1936거제시 장목면 관포리해역2017신요철형어초34°59.213'128°14.997'
1937거제시 남부면 다포리해역2017사각형어초34°43.306'128°39.312'
1938거제시 동부면 수산해역2017신요철형어초34°46.598'128°38.837'

Duplicate rows

Most frequently occurring

지역명시설년도어초종류확인좌표(시)(위도E)확인좌표(분)(위도E)확인좌표(시)(경도N)확인좌표(분)(경도N)# duplicates
0통영 산양 추도2003상자형강제어초34˚45.234'128˚19.962'2
1통영 한산 비진외항2001사각형어초34˚41.896'128˚28.648'2
2통영 한산 비진외항2001사각형어초34˚42.042'128˚28.634'2