Overview

Dataset statistics

Number of variables6
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory55.0 B

Variable types

Numeric1
Text4
Categorical1

Dataset

Description연도별 태풍피해(인명 및 재산피해, 복구액 등)의 전국 대비 전라남도의 피해 현황에 대한 자료를 조회할 수 있습니다.
Author전라남도
URLhttps://www.data.go.kr/data/15124441/fileData.do

Alerts

태풍명 has unique valuesUnique
발생기간 has unique valuesUnique
재산피해규모 전국(전남)_억 원 has unique valuesUnique
복구액 전국(전남)_억 원 has unique valuesUnique

Reproduction

Analysis started2023-12-12 19:04:17.487672
Analysis finished2023-12-12 19:04:18.046061
Duration0.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct13
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.5455
Minimum2005
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T04:04:18.111339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2006.05
Q12010.25
median2015
Q32019
95-th percentile2020.95
Maximum2022
Range17
Interquartile range (IQR)8.75

Descriptive statistics

Standard deviation5.2438379
Coefficient of variation (CV)0.0026029881
Kurtosis-1.2258328
Mean2014.5455
Median Absolute Deviation (MAD)4
Skewness-0.30968339
Sum44320
Variance27.497835
MonotonicityIncreasing
2023-12-13T04:04:18.249305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2019 4
18.2%
2010 3
13.6%
2012 3
13.6%
2018 2
9.1%
2020 2
9.1%
2005 1
 
4.5%
2006 1
 
4.5%
2007 1
 
4.5%
2011 1
 
4.5%
2014 1
 
4.5%
Other values (3) 3
13.6%
ValueCountFrequency (%)
2005 1
 
4.5%
2006 1
 
4.5%
2007 1
 
4.5%
2010 3
13.6%
2011 1
 
4.5%
2012 3
13.6%
2014 1
 
4.5%
2016 1
 
4.5%
2018 2
9.1%
2019 4
18.2%
ValueCountFrequency (%)
2022 1
 
4.5%
2021 1
 
4.5%
2020 2
9.1%
2019 4
18.2%
2018 2
9.1%
2016 1
 
4.5%
2014 1
 
4.5%
2012 3
13.6%
2011 1
 
4.5%
2010 3
13.6%

태풍명
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-13T04:04:18.428553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.8636364
Min length2

Characters and Unicode

Total characters63
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row나비
2nd row산산
3rd row나리
4th row덴무
5th row곤파스
ValueCountFrequency (%)
나비 1
 
4.2%
산산 1
 
4.2%
오마이스 1
 
4.2%
하이선 1
 
4.2%
마이삭 1
 
4.2%
바비 1
 
4.2%
미탁 1
 
4.2%
타파 1
 
4.2%
링링 1
 
4.2%
다나스 1
 
4.2%
Other values (14) 14
58.3%
2023-12-13T04:04:18.735456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 5
 
7.9%
나 4
 
6.3%
파 3
 
4.8%
스 3
 
4.8%
바 3
 
4.8%
산 3
 
4.8%
무 2
 
3.2%
, 2
 
3.2%
덴 2
 
3.2%
리 2
 
3.2%
Other values (30) 34
54.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59
93.7%
Other Punctuation 2
 
3.2%
Space Separator 2
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 5
 
8.5%
나 4
 
6.8%
파 3
 
5.1%
스 3
 
5.1%
바 3
 
5.1%
산 3
 
5.1%
무 2
 
3.4%
덴 2
 
3.4%
리 2
 
3.4%
링 2
 
3.4%
Other values (28) 30
50.8%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59
93.7%
Common 4
 
6.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 5
 
8.5%
나 4
 
6.8%
파 3
 
5.1%
스 3
 
5.1%
바 3
 
5.1%
산 3
 
5.1%
무 2
 
3.4%
덴 2
 
3.4%
리 2
 
3.4%
링 2
 
3.4%
Other values (28) 30
50.8%
Common
ValueCountFrequency (%)
, 2
50.0%
2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59
93.7%
ASCII 4
 
6.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 5
 
8.5%
나 4
 
6.8%
파 3
 
5.1%
스 3
 
5.1%
바 3
 
5.1%
산 3
 
5.1%
무 2
 
3.4%
덴 2
 
3.4%
리 2
 
3.4%
링 2
 
3.4%
Other values (28) 30
50.8%
ASCII
ValueCountFrequency (%)
, 2
50.0%
2
50.0%

발생기간
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-13T04:04:18.921463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters462
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row2005-09-06~2005-09-18
2nd row2006-09-16~2006-09-18
3rd row2007-09-13~2007-09-18
4th row2010-08-09~2010-08-10
5th row2010-09-01~2010-09-03
ValueCountFrequency (%)
2005-09-06~2005-09-18 1
 
4.5%
2006-09-16~2006-09-18 1
 
4.5%
2021-08-22~2021-08-24 1
 
4.5%
2020-09-02~2020-09-07 1
 
4.5%
2020-08-26~2020-08-27 1
 
4.5%
2019-10-02~2019-10-04 1
 
4.5%
2019-09-21~2019-09-22 1
 
4.5%
2019-09-06~2019-09-08 1
 
4.5%
2019-07-19~2019-07-20 1
 
4.5%
2018-10-05~2018-10-06 1
 
4.5%
Other values (12) 12
54.5%
2023-12-13T04:04:19.222757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 129
27.9%
- 88
19.0%
2 74
16.0%
1 55
11.9%
9 31
 
6.7%
~ 22
 
4.8%
8 22
 
4.8%
7 12
 
2.6%
6 10
 
2.2%
4 8
 
1.7%
Other values (2) 11
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 352
76.2%
Dash Punctuation 88
 
19.0%
Math Symbol 22
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 129
36.6%
2 74
21.0%
1 55
15.6%
9 31
 
8.8%
8 22
 
6.2%
7 12
 
3.4%
6 10
 
2.8%
4 8
 
2.3%
5 6
 
1.7%
3 5
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 88
100.0%
Math Symbol
ValueCountFrequency (%)
~ 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 129
27.9%
- 88
19.0%
2 74
16.0%
1 55
11.9%
9 31
 
6.7%
~ 22
 
4.8%
8 22
 
4.8%
7 12
 
2.6%
6 10
 
2.2%
4 8
 
1.7%
Other values (2) 11
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 129
27.9%
- 88
19.0%
2 74
16.0%
1 55
11.9%
9 31
 
6.7%
~ 22
 
4.8%
8 22
 
4.8%
7 12
 
2.6%
6 10
 
2.2%
4 8
 
1.7%
Other values (2) 11
 
2.4%

인명피해 규모 전국(전남)_명
Categorical

Distinct10
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
0(0)
7 
6(0)
4 
1(0)
3 
2(0)
2 
15(2)
1 
Other values (5)
5 

Length

Max length5
Median length4
Mean length4.1818182
Min length4

Unique

Unique6 ?
Unique (%)27.3%

Sample

1st row6(0)
2nd row6(0)
3rd row15(2)
4th row1(0)
5th row6(0)

Common Values

ValueCountFrequency (%)
0(0) 7
31.8%
6(0) 4
18.2%
1(0) 3
13.6%
2(0) 2
 
9.1%
15(2) 1
 
4.5%
11(3) 1
 
4.5%
4(0) 1
 
4.5%
1(1) 1
 
4.5%
15(0) 1
 
4.5%
12(0) 1
 
4.5%

Length

2023-12-13T04:04:19.376110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:04:19.568809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0(0 7
31.8%
6(0 4
18.2%
1(0 3
13.6%
2(0 2
 
9.1%
15(2 1
 
4.5%
11(3 1
 
4.5%
4(0 1
 
4.5%
1(1 1
 
4.5%
15(0 1
 
4.5%
12(0 1
 
4.5%

재산피해규모 전국(전남)_억 원
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-13T04:04:19.796709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length7.5
Min length4

Characters and Unicode

Total characters165
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row1385(0.04)
2nd row109(0.2)
3rd row1592(620)
4th row34(11)
5th row1674(70)
ValueCountFrequency (%)
1385(0.04 1
 
4.5%
109(0.2 1
 
4.5%
211(0.52 1
 
4.5%
2214(21 1
 
4.5%
36(20 1
 
4.5%
1677(107 1
 
4.5%
23(12 1
 
4.5%
333(77 1
 
4.5%
6(3 1
 
4.5%
549(84 1
 
4.5%
Other values (12) 12
54.5%
2023-12-13T04:04:20.178046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 22
13.3%
) 22
13.3%
1 19
11.5%
2 18
10.9%
3 16
9.7%
0 13
7.9%
7 13
7.9%
4 10
6.1%
6 10
6.1%
5 9
5.5%
Other values (3) 13
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 117
70.9%
Open Punctuation 22
 
13.3%
Close Punctuation 22
 
13.3%
Other Punctuation 4
 
2.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 19
16.2%
2 18
15.4%
3 16
13.7%
0 13
11.1%
7 13
11.1%
4 10
8.5%
6 10
8.5%
5 9
7.7%
9 5
 
4.3%
8 4
 
3.4%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 165
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
( 22
13.3%
) 22
13.3%
1 19
11.5%
2 18
10.9%
3 16
9.7%
0 13
7.9%
7 13
7.9%
4 10
6.1%
6 10
6.1%
5 9
5.5%
Other values (3) 13
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 165
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 22
13.3%
) 22
13.3%
1 19
11.5%
2 18
10.9%
3 16
9.7%
0 13
7.9%
7 13
7.9%
4 10
6.1%
6 10
6.1%
5 9
5.5%
Other values (3) 13
7.9%

복구액 전국(전남)_억 원
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-13T04:04:20.776231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length8.2272727
Min length5

Characters and Unicode

Total characters181
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row2595(3)
2nd row152(0.2)
3rd row3636(1560)
4th row72(18)
5th row1793(143)
ValueCountFrequency (%)
2595(3 1
 
4.5%
152(0.2 1
 
4.5%
1049(0.17 1
 
4.5%
6063(415 1
 
4.5%
110(61 1
 
4.5%
9388(166 1
 
4.5%
183(85 1
 
4.5%
1590(719 1
 
4.5%
21(11 1
 
4.5%
2361(113 1
 
4.5%
Other values (12) 12
54.5%
2023-12-13T04:04:21.159118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 31
17.1%
( 22
12.2%
) 22
12.2%
5 16
8.8%
3 15
8.3%
0 13
7.2%
6 13
7.2%
4 11
 
6.1%
8 10
 
5.5%
2 9
 
5.0%
Other values (3) 19
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134
74.0%
Open Punctuation 22
 
12.2%
Close Punctuation 22
 
12.2%
Other Punctuation 3
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 31
23.1%
5 16
11.9%
3 15
11.2%
0 13
9.7%
6 13
9.7%
4 11
 
8.2%
8 10
 
7.5%
2 9
 
6.7%
9 8
 
6.0%
7 8
 
6.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 181
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 31
17.1%
( 22
12.2%
) 22
12.2%
5 16
8.8%
3 15
8.3%
0 13
7.2%
6 13
7.2%
4 11
 
6.1%
8 10
 
5.5%
2 9
 
5.0%
Other values (3) 19
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 181
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 31
17.1%
( 22
12.2%
) 22
12.2%
5 16
8.8%
3 15
8.3%
0 13
7.2%
6 13
7.2%
4 11
 
6.1%
8 10
 
5.5%
2 9
 
5.0%
Other values (3) 19
10.5%

Interactions

2023-12-13T04:04:17.724311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:04:21.281840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도태풍명발생기간인명피해 규모 전국(전남)_명재산피해규모 전국(전남)_억 원복구액 전국(전남)_억 원
연도1.0001.0001.0000.0001.0001.000
태풍명1.0001.0001.0001.0001.0001.000
발생기간1.0001.0001.0001.0001.0001.000
인명피해 규모 전국(전남)_명0.0001.0001.0001.0001.0001.000
재산피해규모 전국(전남)_억 원1.0001.0001.0001.0001.0001.000
복구액 전국(전남)_억 원1.0001.0001.0001.0001.0001.000
2023-12-13T04:04:21.470938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도인명피해 규모 전국(전남)_명
연도1.0000.000
인명피해 규모 전국(전남)_명0.0001.000

Missing values

2023-12-13T04:04:17.864726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:04:17.987741image/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

연도태풍명발생기간인명피해 규모 전국(전남)_명재산피해규모 전국(전남)_억 원복구액 전국(전남)_억 원
02005나비2005-09-06~2005-09-186(0)1385(0.04)2595(3)
12006산산2006-09-16~2006-09-186(0)109(0.2)152(0.2)
22007나리2007-09-13~2007-09-1815(2)1592(620)3636(1560)
32010덴무2010-08-09~2010-08-101(0)34(11)72(18)
42010곤파스2010-09-01~2010-09-036(0)1674(70)1793(143)
52010말로2010-09-05~2010-09-070(0)16(0.2)4(0.06)
62011무이파2011-08-06~2011-08-101(0)2183(851)4617(1654)
72012카눈2012-07-17~2012-07-191(0)15(4)35(8)
82012볼라벤, 덴빈2012-08-25~2012-08-3011(3)6365(3713)10113(6217)
92012산바2012-09-14~2012-09-172(0)3657(322)8415(559)
연도태풍명발생기간인명피해 규모 전국(전남)_명재산피해규모 전국(전남)_억 원복구액 전국(전남)_억 원
122018솔릭2018-08-23~2018-08-240(0)93(77)382(342)
132018콩레이2018-10-05~2018-10-062(0)549(84)2361(113)
142019다나스2019-07-19~2019-07-200(0)6(3)21(11)
152019링링2019-09-06~2019-09-084(0)333(77)1590(719)
162019타파2019-09-21~2019-09-221(1)23(12)183(85)
172019미탁2019-10-02~2019-10-0415(0)1677(107)9388(166)
182020바비2020-08-26~2020-08-270(0)36(20)110(61)
192020마이삭, 하이선2020-09-02~2020-09-0712(0)2214(21)6063(415)
202021오마이스2021-08-22~2021-08-240(0)211(0.52)1049(0.17)
212022힌남노2022-09-03~2022-09-070(0)2440(27)7445(385)