Overview

Dataset statistics

Number of variables4
Number of observations51
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory34.6 B

Variable types

Categorical1
Text2
DateTime1

Dataset

Description소방서별 안전센터 현황, 설치일자, 관할구역 정보
Author강원도
URLhttps://www.data.go.kr/data/15056066/fileData.do

Alerts

119안전센터명 has unique valuesUnique
관할구역 has unique valuesUnique

Reproduction

Analysis started2023-12-12 02:16:40.730413
Analysis finished2023-12-12 02:16:41.183516
Duration0.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소방서명
Categorical

Distinct14
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Memory size540.0 B
춘천
8 
속초
7 
원주
6 
강릉
5 
동해
3 
Other values (9)
22 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
춘천 8
15.7%
속초 7
13.7%
원주 6
11.8%
강릉 5
9.8%
동해 3
 
5.9%
삼척 3
 
5.9%
정선 3
 
5.9%
태백 3
 
5.9%
평창 3
 
5.9%
영월 2
 
3.9%
Other values (4) 8
15.7%

Length

2023-12-12T11:16:41.261891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
춘천 8
15.7%
속초 7
13.7%
원주 6
11.8%
강릉 5
9.8%
동해 3
 
5.9%
삼척 3
 
5.9%
정선 3
 
5.9%
태백 3
 
5.9%
평창 3
 
5.9%
영월 2
 
3.9%
Other values (4) 8
15.7%

119안전센터명
Text

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-12T11:16:41.590579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.0784314
Min length2

Characters and Unicode

Total characters106
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)100.0%

Sample

1st row경포
2nd row내곡
3rd row주문진
4th row포남
5th row옥계
ValueCountFrequency (%)
경포 1
 
2.0%
원통 1
 
2.0%
고한 1
 
2.0%
신동 1
 
2.0%
임계 1
 
2.0%
동송 1
 
2.0%
김화 1
 
2.0%
소양 1
 
2.0%
효자 1
 
2.0%
석사 1
 
2.0%
Other values (41) 41
80.4%
2023-12-12T11:16:42.042173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
양 5
 
4.7%
북 4
 
3.8%
동 4
 
3.8%
내 3
 
2.8%
원 3
 
2.8%
화 3
 
2.8%
성 3
 
2.8%
신 3
 
2.8%
진 3
 
2.8%
계 3
 
2.8%
Other values (58) 72
67.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 106
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 5
 
4.7%
북 4
 
3.8%
동 4
 
3.8%
내 3
 
2.8%
원 3
 
2.8%
화 3
 
2.8%
성 3
 
2.8%
신 3
 
2.8%
진 3
 
2.8%
계 3
 
2.8%
Other values (58) 72
67.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 106
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 5
 
4.7%
북 4
 
3.8%
동 4
 
3.8%
내 3
 
2.8%
원 3
 
2.8%
화 3
 
2.8%
성 3
 
2.8%
신 3
 
2.8%
진 3
 
2.8%
계 3
 
2.8%
Other values (58) 72
67.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 106
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
양 5
 
4.7%
북 4
 
3.8%
동 4
 
3.8%
내 3
 
2.8%
원 3
 
2.8%
화 3
 
2.8%
성 3
 
2.8%
신 3
 
2.8%
진 3
 
2.8%
계 3
 
2.8%
Other values (58) 72
67.9%

설치일자
Date

Distinct27
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Memory size540.0 B
Minimum1972-01-25 00:00:00
Maximum2013-07-26 00:00:00
2023-12-12T11:16:42.211308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:42.350451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)

관할구역
Text

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-12T11:16:42.698889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length27
Mean length18.627451
Min length6

Characters and Unicode

Total characters950
Distinct characters127
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

Unique51 ?
Unique (%)100.0%

Sample

1st row강릉시 사천면, 경포동, 초당동일원
2nd row강릉시 성산면, 왕산면, 구정면, 홍제동, 내곡동, 강남동
3rd row강릉시 주문진읍, 연곡면 일원
4th row강릉시 포남2동, 송정동 일원 및 성덕동 일부
5th row강릉시 강동면, 옥계면 일원
ValueCountFrequency (%)
일원 49
 
19.9%
및 7
 
2.8%
원주시 6
 
2.4%
일부 6
 
2.4%
서면 5
 
2.0%
춘천시 5
 
2.0%
강릉시 5
 
2.0%
남면 4
 
1.6%
고성군 3
 
1.2%
삼척시 3
 
1.2%
Other values (132) 153
62.2%
2023-12-12T11:16:43.269112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
195
20.5%
, 75
 
7.9%
면 70
 
7.4%
동 61
 
6.4%
원 60
 
6.3%
일 57
 
6.0%
시 27
 
2.8%
군 24
 
2.5%
읍 16
 
1.7%
천 13
 
1.4%
Other values (117) 352
37.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 673
70.8%
Space Separator 195
 
20.5%
Other Punctuation 75
 
7.9%
Decimal Number 5
 
0.5%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 70
 
10.4%
동 61
 
9.1%
원 60
 
8.9%
일 57
 
8.5%
시 27
 
4.0%
군 24
 
3.6%
읍 16
 
2.4%
천 13
 
1.9%
남 13
 
1.9%
성 11
 
1.6%
Other values (111) 321
47.7%
Decimal Number
ValueCountFrequency (%)
2 3
60.0%
1 2
40.0%
Space Separator
ValueCountFrequency (%)
195
100.0%
Other Punctuation
ValueCountFrequency (%)
, 75
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 673
70.8%
Common 277
29.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 70
 
10.4%
동 61
 
9.1%
원 60
 
8.9%
일 57
 
8.5%
시 27
 
4.0%
군 24
 
3.6%
읍 16
 
2.4%
천 13
 
1.9%
남 13
 
1.9%
성 11
 
1.6%
Other values (111) 321
47.7%
Common
ValueCountFrequency (%)
195
70.4%
, 75
 
27.1%
2 3
 
1.1%
1 2
 
0.7%
( 1
 
0.4%
) 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 673
70.8%
ASCII 277
29.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
195
70.4%
, 75
 
27.1%
2 3
 
1.1%
1 2
 
0.7%
( 1
 
0.4%
) 1
 
0.4%
Hangul
ValueCountFrequency (%)
면 70
 
10.4%
동 61
 
9.1%
원 60
 
8.9%
일 57
 
8.5%
시 27
 
4.0%
군 24
 
3.6%
읍 16
 
2.4%
천 13
 
1.9%
남 13
 
1.9%
성 11
 
1.6%
Other values (111) 321
47.7%

Correlations

2023-12-12T11:16:43.412824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소방서명119안전센터명설치일자관할구역
소방서명1.0001.0000.6351.000
119안전센터명1.0001.0001.0001.000
설치일자0.6351.0001.0001.000
관할구역1.0001.0001.0001.000

Missing values

2023-12-12T11:16:41.001678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:16:41.133330image/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

소방서명119안전센터명설치일자관할구역
0강릉경포1985-06-03강릉시 사천면, 경포동, 초당동일원
1강릉내곡1994-05-10강릉시 성산면, 왕산면, 구정면, 홍제동, 내곡동, 강남동
2강릉주문진1987-09-07강릉시 주문진읍, 연곡면 일원
3강릉포남2000-07-15강릉시 포남2동, 송정동 일원 및 성덕동 일부
4강릉옥계2000-07-15강릉시 강동면, 옥계면 일원
5동해묵호1981-12-11동해시 발한동, 묵호동, 망상동, 동호동 일원
6동해북삼1984-02-07동해시 북삼동, 삼화동 일원
7동해북평1991-05-20동해시 북평동, 송정동 일원
8삼척도계1991-05-20삼척시 도계읍, 신기면, 하장면 일원
9삼척원덕1995-01-01삼척시 원덕읍, 가곡면 일원
소방서명119안전센터명설치일자관할구역
41태백장성1983-04-08태백시 장성동, 문곡소도동 일원
42태백철암1985-06-03태백시 구문소동, 철암동 일원
43태백화전1990-02-15태백시 황연동 일원 및 삼수동 일부
44평창진부1998-01-17평창군 진부면 일원
45평창봉평2008-09-05평창군 봉평면, 용평면 일원
46평창대관령2010-10-29평창군 대관령면 일원
47홍천양덕원1990-02-15홍천군 남면, 서면 일원
48홍천서석2011-12-23홍천군 서석면, 내촌면, 내면 일원
49횡성둔내2000-07-15횡성군 둔내면, 청일면 일원
50횡성우천2010-04-02횡성군 우천면, 안흥면, 강림면 일원