Overview

Dataset statistics

Number of variables7
Number of observations84
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory58.6 B

Variable types

Numeric1
DateTime3
Text2
Categorical1

Dataset

Description각종 재해현장의 긴급구조활동 및 정보파악 현장활동 지원 등을 위한 대상별, 상황별 연간 현장출동현황 데이터를 제공합니다. (연번, 일자, 발생, 종료, 장소, 종별, 내용 순으로 데이터 작성)
URLhttps://www.data.go.kr/data/15081103/fileData.do

Alerts

종별 is highly imbalanced (73.6%)Imbalance
연번 has unique valuesUnique
발생 has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:40:19.523398
Analysis finished2023-12-12 03:40:20.682961
Duration1.16 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.5
Minimum1
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-12T12:40:20.799838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.15
Q121.75
median42.5
Q363.25
95-th percentile79.85
Maximum84
Range83
Interquartile range (IQR)41.5

Descriptive statistics

Standard deviation24.392622
Coefficient of variation (CV)0.57394404
Kurtosis-1.2
Mean42.5
Median Absolute Deviation (MAD)21
Skewness0
Sum3570
Variance595
MonotonicityStrictly increasing
2023-12-12T12:40:21.007744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
55 1
 
1.2%
63 1
 
1.2%
62 1
 
1.2%
61 1
 
1.2%
60 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
57 1
 
1.2%
56 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
84 1
1.2%
83 1
1.2%
82 1
1.2%
81 1
1.2%
80 1
1.2%
79 1
1.2%
78 1
1.2%
77 1
1.2%
76 1
1.2%
75 1
1.2%

일자
Date

Distinct74
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Memory size804.0 B
Minimum2022-01-08 00:00:00
Maximum2022-12-27 00:00:00
2023-12-12T12:40:21.213341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:21.405647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

발생
Date

UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size804.0 B
Minimum2023-12-12 00:07:00
Maximum2023-12-12 23:48:00
2023-12-12T12:40:21.620799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:21.798115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

종료
Date

Distinct80
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size804.0 B
Minimum2023-12-12 00:04:00
Maximum2023-12-12 23:43:00
2023-12-12T12:40:21.981111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:22.145851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

장소
Text

Distinct54
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Memory size804.0 B
2023-12-12T12:40:22.382151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6.5
Mean length6.3809524
Min length5

Characters and Unicode

Total characters536
Distinct characters67
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)38.1%

Sample

1st row대덕구 오정동
2nd row서구 월평동
3rd row동구 정동
4th row대덕구 문평동
5th row대덕구 오정동
ValueCountFrequency (%)
대덕구 24
 
14.3%
유성구 17
 
10.1%
동구 16
 
9.5%
서구 14
 
8.3%
중구 13
 
7.7%
오정동 5
 
3.0%
대화동 4
 
2.4%
문화동 3
 
1.8%
읍내동 3
 
1.8%
봉명동 3
 
1.8%
Other values (49) 66
39.3%
2023-12-12T12:40:23.170044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 100
18.7%
구 84
15.7%
84
15.7%
대 33
 
6.2%
덕 26
 
4.9%
성 18
 
3.4%
유 17
 
3.2%
서 16
 
3.0%
중 15
 
2.8%
정 11
 
2.1%
Other values (57) 132
24.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 452
84.3%
Space Separator 84
 
15.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 100
22.1%
구 84
18.6%
대 33
 
7.3%
덕 26
 
5.8%
성 18
 
4.0%
유 17
 
3.8%
서 16
 
3.5%
중 15
 
3.3%
정 11
 
2.4%
화 9
 
2.0%
Other values (56) 123
27.2%
Space Separator
ValueCountFrequency (%)
84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 452
84.3%
Common 84
 
15.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 100
22.1%
구 84
18.6%
대 33
 
7.3%
덕 26
 
5.8%
성 18
 
4.0%
유 17
 
3.8%
서 16
 
3.5%
중 15
 
3.3%
정 11
 
2.4%
화 9
 
2.0%
Other values (56) 123
27.2%
Common
ValueCountFrequency (%)
84
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 452
84.3%
ASCII 84
 
15.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 100
22.1%
구 84
18.6%
대 33
 
7.3%
덕 26
 
5.8%
성 18
 
4.0%
유 17
 
3.8%
서 16
 
3.5%
중 15
 
3.3%
정 11
 
2.4%
화 9
 
2.0%
Other values (56) 123
27.2%
ASCII
ValueCountFrequency (%)
84
100.0%

종별
Categorical

IMBALANCE 

Distinct4
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size804.0 B
화재
77 
구조
 
4
오인
 
2
붕괴
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row화재
2nd row화재
3rd row화재
4th row구조
5th row화재

Common Values

ValueCountFrequency (%)
화재 77
91.7%
구조 4
 
4.8%
오인 2
 
2.4%
붕괴 1
 
1.2%

Length

2023-12-12T12:40:23.321729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:40:23.490219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
화재 77
91.7%
구조 4
 
4.8%
오인 2
 
2.4%
붕괴 1
 
1.2%

내용
Text

Distinct49
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Memory size804.0 B
2023-12-12T12:40:23.717909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length6.0238095
Min length4

Characters and Unicode

Total characters506
Distinct characters104
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

Unique35 ?
Unique (%)41.7%

Sample

1st row아파트 화재
2nd row아파트 화재
3rd row여관 화재
4th row공장 위험물 누출
5th row 육류 냉동창고장 화재
ValueCountFrequency (%)
화재 55
35.3%
아파트 11
 
7.1%
공장 7
 
4.5%
빌라 5
 
3.2%
공장화재 5
 
3.2%
주택화재 4
 
2.6%
주택 4
 
2.6%
상가 3
 
1.9%
창고 3
 
1.9%
비닐하우스 2
 
1.3%
Other values (51) 57
36.5%
2023-12-12T12:40:24.140469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
화 76
15.0%
재 75
14.8%
73
 
14.4%
장 20
 
4.0%
공 15
 
3.0%
아 14
 
2.8%
트 13
 
2.6%
파 13
 
2.6%
주 12
 
2.4%
고 9
 
1.8%
Other values (94) 186
36.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 426
84.2%
Space Separator 73
 
14.4%
Close Punctuation 3
 
0.6%
Open Punctuation 3
 
0.6%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
화 76
17.8%
재 75
17.6%
장 20
 
4.7%
공 15
 
3.5%
아 14
 
3.3%
트 13
 
3.1%
파 13
 
3.1%
주 12
 
2.8%
고 9
 
2.1%
택 9
 
2.1%
Other values (90) 170
39.9%
Space Separator
ValueCountFrequency (%)
73
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 426
84.2%
Common 80
 
15.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
화 76
17.8%
재 75
17.6%
장 20
 
4.7%
공 15
 
3.5%
아 14
 
3.3%
트 13
 
3.1%
파 13
 
3.1%
주 12
 
2.8%
고 9
 
2.1%
택 9
 
2.1%
Other values (90) 170
39.9%
Common
ValueCountFrequency (%)
73
91.2%
) 3
 
3.8%
( 3
 
3.8%
1 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 426
84.2%
ASCII 80
 
15.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
화 76
17.8%
재 75
17.6%
장 20
 
4.7%
공 15
 
3.5%
아 14
 
3.3%
트 13
 
3.1%
파 13
 
3.1%
주 12
 
2.8%
고 9
 
2.1%
택 9
 
2.1%
Other values (90) 170
39.9%
ASCII
ValueCountFrequency (%)
73
91.2%
) 3
 
3.8%
( 3
 
3.8%
1 1
 
1.2%

Interactions

2023-12-12T12:40:20.247180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:40:24.284790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번일자발생종료장소종별내용
연번1.0001.0001.0000.9100.0000.3810.000
일자1.0001.0001.0000.9540.9401.0000.946
발생1.0001.0001.0001.0001.0001.0001.000
종료0.9100.9541.0001.0000.9811.0000.970
장소0.0000.9401.0000.9811.0000.0000.909
종별0.3811.0001.0001.0000.0001.0000.913
내용0.0000.9461.0000.9700.9090.9131.000
2023-12-12T12:40:24.425830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번종별
연번1.0000.225
종별0.2251.000

Missing values

2023-12-12T12:40:20.425081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:40:20.609851image/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

연번일자발생종료장소종별내용
012022-01-0813:2714:47대덕구 오정동화재아파트 화재
122022-01-1017:1218:20서구 월평동화재아파트 화재
232022-01-1216:1618:20동구 정동화재여관 화재
342022-01-1518:2722:39대덕구 문평동구조공장 위험물 누출
452022-01-1711:5113:47대덕구 오정동화재육류 냉동창고장 화재
562022-01-2017:4618:48서구 원정동화재비닐하우스 화재
672022-01-2117:1919:49동구 자양동화재주택 화재
782022-01-2414:5316:30대덕구 대화동화재아파트 화재
892022-01-2507:5408:25중구 석교동화재창고 화재
9102022-01-3104:5906:41동구 자양동화재주택 보일러실 화재
연번일자발생종료장소종별내용
74752022-11-1215:4515:58유성구 궁동오인지하층 화재
75762022-11-1303:5205:27유성구 대정동화재주택화재
76772022-11-1419:3720:21유성구 덕진동화재원자력연구원 화재
77782022-11-1603:4205:40대덕구 연축동화재고물상 화재
78792022-11-1615:4717:23중구 중촌동화재점포 화재
79802022-11-1905:0005:57중구 석교동화재다세대주택 화재
80812022-12-1603:3404:55서구 탄방동화재창고 화재
81822022-12-2017:3017:55대덕구 문평동화재공장 화재
82832022-12-2109:5710:03대덕구 송촌동화재학교 화재
83842022-12-2721:5023:09대덕구 오정동화재공장화재