Overview

Dataset statistics

Number of variables7
Number of observations66
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory58.0 B

Variable types

Categorical4
Text2
DateTime1

Dataset

Description경상남도 양산시 관내 설치된 재해 예경보시스템 현황입니다. 시설의 종류, 설치 위치, 설치년도 및 개요를 제공합니다.
Author경상남도 양산시
URLhttps://www.data.go.kr/data/15104909/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
구분 is highly overall correlated with 설치년도 and 1 other fieldsHigh correlation
설치년도 is highly overall correlated with 구분 and 2 other fieldsHigh correlation
시설개요(대수) is highly overall correlated with 설치년도 and 1 other fieldsHigh correlation
비고 is highly overall correlated with 구분 and 2 other fieldsHigh correlation
시설개요(대수) is highly imbalanced (66.5%)Imbalance

Reproduction

Analysis started2023-12-12 13:41:48.682528
Analysis finished2023-12-12 13:41:49.415496
Duration0.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size660.0 B
재난감시CCTV
15 
재해문자 전광판
13 
자동기상관측장비(AWS)
10 
강우량계
8 
자동우량 경보시스템
5 
Other values (4)
15 

Length

Max length13
Median length10
Mean length8.3939394
Min length4

Unique

Unique1 ?
Unique (%)1.5%

Sample

1st row자동우량 경보시스템
2nd row자동우량 경보시스템
3rd row자동우량 경보시스템
4th row자동우량 경보시스템
5th row자동우량 경보시스템

Common Values

ValueCountFrequency (%)
재난감시CCTV 15
22.7%
재해문자 전광판 13
19.7%
자동기상관측장비(AWS) 10
15.2%
강우량계 8
12.1%
자동우량 경보시스템 5
 
7.6%
라디오재난방송시스템 5
 
7.6%
수위_적설계 5
 
7.6%
음성경보시스템 4
 
6.1%
지진가속도계측시스템 1
 
1.5%

Length

2023-12-12T22:41:49.487977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:41:49.607080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재난감시cctv 15
17.9%
재해문자 13
15.5%
전광판 13
15.5%
자동기상관측장비(aws 10
11.9%
강우량계 8
9.5%
자동우량 5
 
6.0%
경보시스템 5
 
6.0%
라디오재난방송시스템 5
 
6.0%
수위_적설계 5
 
6.0%
음성경보시스템 4
 
4.8%

위치
Text

Distinct36
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Memory size660.0 B
2023-12-12T22:41:49.834280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20.5
Mean length5.9090909
Min length2

Characters and Unicode

Total characters390
Distinct characters88
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

Unique27 ?
Unique (%)40.9%

Sample

1st row하북면 용연리
2nd row평산동
3rd row원동면 내포리
4th row원동면 영포리
5th row명동
ValueCountFrequency (%)
원동면 22
25.0%
동면 8
 
9.1%
평산동 5
 
5.7%
하북면 4
 
4.5%
물금읍 3
 
3.4%
명동 2
 
2.3%
강서동 2
 
2.3%
내포리 2
 
2.3%
서창동 2
 
2.3%
상북면 2
 
2.3%
Other values (33) 36
40.9%
2023-12-12T22:41:50.195158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 54
 
13.8%
면 39
 
10.0%
원 24
 
6.2%
22
 
5.6%
북 11
 
2.8%
( 10
 
2.6%
) 10
 
2.6%
산 8
 
2.1%
하 7
 
1.8%
소 7
 
1.8%
Other values (78) 198
50.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 321
82.3%
Decimal Number 27
 
6.9%
Space Separator 22
 
5.6%
Open Punctuation 10
 
2.6%
Close Punctuation 10
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 54
 
16.8%
면 39
 
12.1%
원 24
 
7.5%
북 11
 
3.4%
산 8
 
2.5%
하 7
 
2.2%
소 7
 
2.2%
리 6
 
1.9%
서 6
 
1.9%
상 6
 
1.9%
Other values (65) 153
47.7%
Decimal Number
ValueCountFrequency (%)
8 4
14.8%
2 3
11.1%
7 3
11.1%
3 3
11.1%
1 3
11.1%
0 3
11.1%
6 3
11.1%
5 3
11.1%
4 1
 
3.7%
9 1
 
3.7%
Space Separator
ValueCountFrequency (%)
22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 321
82.3%
Common 69
 
17.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 54
 
16.8%
면 39
 
12.1%
원 24
 
7.5%
북 11
 
3.4%
산 8
 
2.5%
하 7
 
2.2%
소 7
 
2.2%
리 6
 
1.9%
서 6
 
1.9%
상 6
 
1.9%
Other values (65) 153
47.7%
Common
ValueCountFrequency (%)
22
31.9%
( 10
14.5%
) 10
14.5%
8 4
 
5.8%
2 3
 
4.3%
7 3
 
4.3%
3 3
 
4.3%
1 3
 
4.3%
0 3
 
4.3%
6 3
 
4.3%
Other values (3) 5
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 321
82.3%
ASCII 69
 
17.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 54
 
16.8%
면 39
 
12.1%
원 24
 
7.5%
북 11
 
3.4%
산 8
 
2.5%
하 7
 
2.2%
소 7
 
2.2%
리 6
 
1.9%
서 6
 
1.9%
상 6
 
1.9%
Other values (65) 153
47.7%
ASCII
ValueCountFrequency (%)
22
31.9%
( 10
14.5%
) 10
14.5%
8 4
 
5.8%
2 3
 
4.3%
7 3
 
4.3%
3 3
 
4.3%
1 3
 
4.3%
0 3
 
4.3%
6 3
 
4.3%
Other values (3) 5
 
7.2%

지구명
Text

Distinct55
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size660.0 B
2023-12-12T22:41:50.454594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length4.2424242
Min length2

Characters and Unicode

Total characters280
Distinct characters96
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)71.2%

Sample

1st row내원사계곡
2nd row무지개폭포
3rd row선장천계곡
4th row어영천계곡
5th row시명골지구
ValueCountFrequency (%)
내원사계곡 3
 
4.5%
무지개폭포 3
 
4.5%
늘밭마을 3
 
4.5%
중앙동 2
 
3.0%
회야강 2
 
3.0%
시명골저수지 2
 
3.0%
에덴벨리 2
 
3.0%
배내골4거리 2
 
3.0%
평산 1
 
1.5%
양산역 1
 
1.5%
Other values (46) 46
68.7%
2023-12-12T22:41:50.818787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 11
 
3.9%
리 10
 
3.6%
곡 9
 
3.2%
계 8
 
2.9%
천 8
 
2.9%
지 8
 
2.9%
내 7
 
2.5%
마 7
 
2.5%
을 7
 
2.5%
장 7
 
2.5%
Other values (86) 198
70.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 274
97.9%
Decimal Number 3
 
1.1%
Uppercase Letter 2
 
0.7%
Space Separator 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 11
 
4.0%
리 10
 
3.6%
곡 9
 
3.3%
계 8
 
2.9%
천 8
 
2.9%
지 8
 
2.9%
내 7
 
2.6%
마 7
 
2.6%
을 7
 
2.6%
장 7
 
2.6%
Other values (81) 192
70.1%
Decimal Number
ValueCountFrequency (%)
4 2
66.7%
3 1
33.3%
Uppercase Letter
ValueCountFrequency (%)
I 1
50.0%
C 1
50.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 274
97.9%
Common 4
 
1.4%
Latin 2
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 11
 
4.0%
리 10
 
3.6%
곡 9
 
3.3%
계 8
 
2.9%
천 8
 
2.9%
지 8
 
2.9%
내 7
 
2.6%
마 7
 
2.6%
을 7
 
2.6%
장 7
 
2.6%
Other values (81) 192
70.1%
Common
ValueCountFrequency (%)
4 2
50.0%
3 1
25.0%
1
25.0%
Latin
ValueCountFrequency (%)
I 1
50.0%
C 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 274
97.9%
ASCII 6
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 11
 
4.0%
리 10
 
3.6%
곡 9
 
3.3%
계 8
 
2.9%
천 8
 
2.9%
지 8
 
2.9%
내 7
 
2.6%
마 7
 
2.6%
을 7
 
2.6%
장 7
 
2.6%
Other values (81) 192
70.1%
ASCII
ValueCountFrequency (%)
4 2
33.3%
3 1
16.7%
I 1
16.7%
C 1
16.7%
1
16.7%

설치년도
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Memory size660.0 B
2015-06
10 
2004-12
9 
2004-07
6 
2010-04
5 
2016-12
4 
Other values (18)
32 

Length

Max length16
Median length7
Mean length6.7272727
Min length4

Unique

Unique7 ?
Unique (%)10.6%

Sample

1st row2001, 2007, 2010
2nd row2004
3rd row2010
4th row2010
5th row2018

Common Values

ValueCountFrequency (%)
2015-06 10
15.2%
2004-12 9
13.6%
2004-07 6
 
9.1%
2010-04 5
 
7.6%
2016-12 4
 
6.1%
2016 3
 
4.5%
2017-05 3
 
4.5%
2002-12 3
 
4.5%
2012-06 2
 
3.0%
2010-06 2
 
3.0%
Other values (13) 19
28.8%

Length

2023-12-12T22:41:50.960767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-06 10
14.7%
2004-12 9
13.2%
2004-07 6
 
8.8%
2010-04 5
 
7.4%
2016-12 4
 
5.9%
2016 3
 
4.4%
2017-05 3
 
4.4%
2002-12 3
 
4.4%
2010 3
 
4.4%
2016-06 2
 
2.9%
Other values (14) 20
29.4%

시설개요(대수)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size660.0 B
1
56 
<NA>
 
5
우량국2, 경보국8, 통제국1, 중계국1, 감시국1
 
1
우량국1, 경보국2, 통제국1
 
1
우량국2, 경보국4
 
1
Other values (2)
 
2

Length

Max length28
Median length1
Mean length2.3030303
Min length1

Unique

Unique5 ?
Unique (%)7.6%

Sample

1st row우량국2, 경보국8, 통제국1, 중계국1, 감시국1
2nd row우량국1, 경보국2, 통제국1
3rd row우량국2, 경보국4
4th row우량국1, 경보국2
5th row우량경보국1, 경보국2

Common Values

ValueCountFrequency (%)
1 56
84.8%
<NA> 5
 
7.6%
우량국2, 경보국8, 통제국1, 중계국1, 감시국1 1
 
1.5%
우량국1, 경보국2, 통제국1 1
 
1.5%
우량국2, 경보국4 1
 
1.5%
우량국1, 경보국2 1
 
1.5%
우량경보국1, 경보국2 1
 
1.5%

Length

2023-12-12T22:41:51.071772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:41:51.194861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 56
74.7%
na 5
 
6.7%
경보국2 3
 
4.0%
우량국2 2
 
2.7%
통제국1 2
 
2.7%
우량국1 2
 
2.7%
경보국8 1
 
1.3%
중계국1 1
 
1.3%
감시국1 1
 
1.3%
경보국4 1
 
1.3%

비고
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size660.0 B
<NA>
41 
2015-06 신품교체
7 
2022-06 신품교체
 
4
적설계(CCTV 포함)
 
3
2019-10 신품교체
 
2
Other values (7)
9 

Length

Max length12
Median length4
Mean length6.7575758
Min length3

Unique

Unique5 ?
Unique (%)7.6%

Sample

1st row2016-05 신품교체
2nd row2018-12 신품교체
3rd row2019-10 신품교체
4th row2019-10 신품교체
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 41
62.1%
2015-06 신품교체 7
 
10.6%
2022-06 신품교체 4
 
6.1%
적설계(CCTV 포함) 3
 
4.5%
2019-10 신품교체 2
 
3.0%
적설계 2
 
3.0%
2017-12 신품교체 2
 
3.0%
2016-05 신품교체 1
 
1.5%
2018-12 신품교체 1
 
1.5%
2016-06 신품교체 1
 
1.5%
Other values (2) 2
 
3.0%

Length

2023-12-12T22:41:51.300626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 41
46.1%
신품교체 20
22.5%
2015-06 7
 
7.9%
2022-06 4
 
4.5%
적설계(cctv 3
 
3.4%
포함 3
 
3.4%
2019-10 2
 
2.2%
적설계 2
 
2.2%
2017-12 2
 
2.2%
2016-05 1
 
1.1%
Other values (4) 4
 
4.5%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size660.0 B
Minimum2022-07-12 00:00:00
Maximum2022-07-12 00:00:00
2023-12-12T22:41:51.412444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:51.539264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Correlations

2023-12-12T22:41:51.607089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분위치지구명설치년도시설개요(대수)비고
구분1.0000.5850.0000.9880.5060.977
위치0.5851.0000.9880.8890.9120.434
지구명0.0000.9881.0000.5820.0001.000
설치년도0.9880.8890.5821.0000.9630.961
시설개요(대수)0.5060.9120.0000.9631.0000.885
비고0.9770.4341.0000.9610.8851.000
2023-12-12T22:41:51.711807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분설치년도시설개요(대수)비고
구분1.0000.8100.3020.787
설치년도0.8101.0000.7140.810
시설개요(대수)0.3020.7141.0000.645
비고0.7870.8100.6451.000
2023-12-12T22:41:51.828882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분설치년도시설개요(대수)비고
구분1.0000.8100.3020.787
설치년도0.8101.0000.7140.810
시설개요(대수)0.3020.7141.0000.645
비고0.7870.8100.6451.000

Missing values

2023-12-12T22:41:49.200180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:41:49.359132image/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

구분위치지구명설치년도시설개요(대수)비고데이터기준일자
0자동우량 경보시스템하북면 용연리내원사계곡2001, 2007, 2010우량국2, 경보국8, 통제국1, 중계국1, 감시국12016-05 신품교체2022-07-12
1자동우량 경보시스템평산동무지개폭포2004우량국1, 경보국2, 통제국12018-12 신품교체2022-07-12
2자동우량 경보시스템원동면 내포리선장천계곡2010우량국2, 경보국42019-10 신품교체2022-07-12
3자동우량 경보시스템원동면 영포리어영천계곡2010우량국1, 경보국22019-10 신품교체2022-07-12
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