Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory116.0 B

Variable types

DateTime1
Categorical7
Text2
Numeric3

Dataset

Description2021년도에 발생한 화재의 일자와 일시, 시군구, 읍면동에 대한 화재 정보이며, 각 화재에 대한 화재원인과 화재피해정보, 화재피해금액에 대한 정보를 포함하고 있음
URLhttps://www.data.go.kr/data/15044005/fileData.do

Alerts

장소대분류 is highly overall correlated with 화재유형 and 1 other fieldsHigh correlation
발화요인소분류 is highly overall correlated with 발화요인대분류High correlation
화재유형 is highly overall correlated with 장소대분류 and 1 other fieldsHigh correlation
발화요인대분류 is highly overall correlated with 발화요인소분류High correlation
장소중분류 is highly overall correlated with 화재유형 and 1 other fieldsHigh correlation
인명피해소계 is highly overall correlated with 부상High correlation
부상 is highly overall correlated with 인명피해소계High correlation
사망 is highly imbalanced (96.3%)Imbalance
재산피해소계 is highly skewed (γ1 = 79.83480239)Skewed
인명피해소계 has 9559 (95.6%) zerosZeros
부상 has 9630 (96.3%) zerosZeros
재산피해소계 has 789 (7.9%) zerosZeros

Reproduction

Analysis started2023-12-11 23:32:17.924753
Analysis finished2023-12-11 23:32:20.350148
Duration2.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

화재발생년월일
Date

Distinct9879
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-01-01 00:43:00
Maximum2021-12-31 22:54:00
2023-12-12T08:32:20.450222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:20.578243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

시도
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기도
2220 
서울특별시
1325 
경상남도
802 
경상북도
763 
전라남도
674 
Other values (12)
4216 

Length

Max length7
Median length5
Mean length4.1208
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기도
2nd row경기도
3rd row전라북도
4th row부산광역시
5th row전라남도

Common Values

ValueCountFrequency (%)
경기도 2220
22.2%
서울특별시 1325
13.2%
경상남도 802
 
8.0%
경상북도 763
 
7.6%
전라남도 674
 
6.7%
부산광역시 637
 
6.4%
전라북도 585
 
5.9%
충청남도 548
 
5.5%
강원도 505
 
5.1%
인천광역시 374
 
3.7%
Other values (7) 1567
15.7%

Length

2023-12-12T08:32:20.738188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 2220
22.2%
서울특별시 1325
13.2%
경상남도 802
 
8.0%
경상북도 763
 
7.6%
전라남도 674
 
6.7%
부산광역시 637
 
6.4%
전라북도 585
 
5.9%
충청남도 548
 
5.5%
강원도 505
 
5.1%
인천광역시 374
 
3.7%
Other values (7) 1567
15.7%

시군구
Text

Distinct228
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T08:32:21.027675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.3652
Min length2

Characters and Unicode

Total characters33652
Distinct characters143
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

Unique0 ?
Unique (%)0.0%

Sample

1st row이천시
2nd row고양시덕양구
3rd row전주시완산구
4th row동래구
5th row화순군
ValueCountFrequency (%)
서구 223
 
2.2%
북구 188
 
1.8%
중구 171
 
1.7%
화성시 158
 
1.5%
동구 154
 
1.5%
창원시 152
 
1.5%
강서구 144
 
1.4%
남구 143
 
1.4%
김해시 124
 
1.2%
평택시 122
 
1.2%
Other values (220) 8695
84.6%
2023-12-12T08:32:21.472758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 4883
 
14.5%
구 4503
 
13.4%
군 2146
 
6.4%
주 1387
 
4.1%
산 966
 
2.9%
천 887
 
2.6%
성 882
 
2.6%
양 835
 
2.5%
서 805
 
2.4%
남 748
 
2.2%
Other values (133) 15610
46.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 33378
99.2%
Space Separator 274
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 4883
 
14.6%
구 4503
 
13.5%
군 2146
 
6.4%
주 1387
 
4.2%
산 966
 
2.9%
천 887
 
2.7%
성 882
 
2.6%
양 835
 
2.5%
서 805
 
2.4%
남 748
 
2.2%
Other values (132) 15336
45.9%
Space Separator
ValueCountFrequency (%)
274
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 33378
99.2%
Common 274
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 4883
 
14.6%
구 4503
 
13.5%
군 2146
 
6.4%
주 1387
 
4.2%
산 966
 
2.9%
천 887
 
2.7%
성 882
 
2.6%
양 835
 
2.5%
서 805
 
2.4%
남 748
 
2.2%
Other values (132) 15336
45.9%
Common
ValueCountFrequency (%)
274
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 33378
99.2%
ASCII 274
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 4883
 
14.6%
구 4503
 
13.5%
군 2146
 
6.4%
주 1387
 
4.2%
산 966
 
2.9%
천 887
 
2.7%
성 882
 
2.6%
양 835
 
2.5%
서 805
 
2.4%
남 748
 
2.2%
Other values (132) 15336
45.9%
ASCII
ValueCountFrequency (%)
274
100.0%

화재유형
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
건축,구조물
6650 
기타(쓰레기 화재등)
1787 
자동차,철도차량
1232 
임야
 
287
선박,항공기
 
41

Length

Max length11
Median length6
Mean length7.0263
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row건축,구조물
2nd row건축,구조물
3rd row기타(쓰레기 화재등)
4th row건축,구조물
5th row건축,구조물

Common Values

ValueCountFrequency (%)
건축,구조물 6650
66.5%
기타(쓰레기 화재등) 1787
 
17.9%
자동차,철도차량 1232
 
12.3%
임야 287
 
2.9%
선박,항공기 41
 
0.4%
위험물,가스제조소등 3
 
< 0.1%

Length

2023-12-12T08:32:21.644110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:32:21.797229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건축,구조물 6650
56.4%
기타(쓰레기 1787
 
15.2%
화재등 1787
 
15.2%
자동차,철도차량 1232
 
10.5%
임야 287
 
2.4%
선박,항공기 41
 
0.3%
위험물,가스제조소등 3
 
< 0.1%

발화요인대분류
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부주의
4672 
전기적 요인
2611 
기계적 요인
1103 
미상
848 
화학적 요인
 
174
Other values (7)
592 

Length

Max length8
Median length7
Mean length4.1293
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row화학적 요인
2nd row전기적 요인
3rd row부주의
4th row자연적인 요인
5th row부주의

Common Values

ValueCountFrequency (%)
부주의 4672
46.7%
전기적 요인 2611
26.1%
기계적 요인 1103
 
11.0%
미상 848
 
8.5%
화학적 요인 174
 
1.7%
기타 140
 
1.4%
교통사고 120
 
1.2%
방화의심 103
 
1.0%
방화 87
 
0.9%
자연적인 요인 53
 
0.5%
Other values (2) 89
 
0.9%

Length

2023-12-12T08:32:21.982714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부주의 4672
33.5%
요인 3941
28.3%
전기적 2611
18.7%
기계적 1103
 
7.9%
미상 848
 
6.1%
화학적 174
 
1.2%
기타 140
 
1.0%
교통사고 120
 
0.9%
방화의심 103
 
0.7%
방화 87
 
0.6%
Other values (3) 142
 
1.0%

발화요인소분류
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
담배꽁초
1408 
미상
848 
미확인단락
778 
음식물 조리중
707 
과열, 과부하
681 
Other values (44)
5578 

Length

Max length21
Median length12
Mean length7.0192
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row화학적 발화(유증기 확산)
2nd row미확인단락
3rd row담배꽁초
4th row자연적 재해
5th row기기(전기, 기계 등) 사용.설치부주의

Common Values

ValueCountFrequency (%)
담배꽁초 1408
14.1%
미상 848
 
8.5%
미확인단락 778
 
7.8%
음식물 조리중 707
 
7.1%
과열, 과부하 681
 
6.8%
불씨,불꽃,화원방치 627
 
6.3%
절연열화에 의한 단락 545
 
5.5%
쓰레기 소각 525
 
5.2%
기기(전기, 기계 등) 사용.설치부주의 385
 
3.9%
기타(부주의) 314
 
3.1%
Other values (39) 3182
31.8%

Length

2023-12-12T08:32:22.139257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
담배꽁초 1408
 
8.5%
단락 1259
 
7.6%
의한 1259
 
7.6%
미상 848
 
5.1%
미확인단락 778
 
4.7%
음식물 707
 
4.3%
조리중 707
 
4.3%
과열 681
 
4.1%
과부하 681
 
4.1%
불씨,불꽃,화원방치 627
 
3.8%
Other values (58) 7610
45.9%

인명피해소계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0619
Minimum0
Maximum13
Zeros9559
Zeros (%)95.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:32:22.284256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.39810079
Coefficient of variation (CV)6.4313536
Kurtosis333.15202
Mean0.0619
Median Absolute Deviation (MAD)0
Skewness14.837153
Sum619
Variance0.15848424
MonotonicityNot monotonic
2023-12-12T08:32:22.409497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 9559
95.6%
1 357
 
3.6%
2 57
 
0.6%
3 11
 
0.1%
6 3
 
< 0.1%
4 3
 
< 0.1%
5 2
 
< 0.1%
7 2
 
< 0.1%
10 2
 
< 0.1%
8 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
ValueCountFrequency (%)
0 9559
95.6%
1 357
 
3.6%
2 57
 
0.6%
3 11
 
0.1%
4 3
 
< 0.1%
5 2
 
< 0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
11 1
 
< 0.1%
10 2
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
7 2
 
< 0.1%
6 3
 
< 0.1%
5 2
 
< 0.1%
4 3
 
< 0.1%
3 11
0.1%

사망
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9915 
1
 
78
2
 
6
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9915
99.2%
1 78
 
0.8%
2 6
 
0.1%
4 1
 
< 0.1%

Length

2023-12-12T08:32:22.530190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:32:22.640294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9915
99.2%
1 78
 
0.8%
2 6
 
0.1%
4 1
 
< 0.1%

부상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0525
Minimum0
Maximum13
Zeros9630
Zeros (%)96.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:32:22.748731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.36763741
Coefficient of variation (CV)7.0026173
Kurtosis364.87695
Mean0.0525
Median Absolute Deviation (MAD)0
Skewness15.506518
Sum525
Variance0.13515727
MonotonicityNot monotonic
2023-12-12T08:32:22.882408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 9630
96.3%
1 300
 
3.0%
2 44
 
0.4%
3 10
 
0.1%
6 4
 
< 0.1%
5 3
 
< 0.1%
4 3
 
< 0.1%
8 2
 
< 0.1%
10 2
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
0 9630
96.3%
1 300
 
3.0%
2 44
 
0.4%
3 10
 
0.1%
4 3
 
< 0.1%
5 3
 
< 0.1%
6 4
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
10 2
 
< 0.1%
8 2
 
< 0.1%
7 1
 
< 0.1%
6 4
 
< 0.1%
5 3
 
< 0.1%
4 3
 
< 0.1%
3 10
 
0.1%
2 44
 
0.4%
1 300
3.0%

재산피해소계
Real number (ℝ)

SKEWED  ZEROS 

Distinct4123
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19605.203
Minimum0
Maximum37747126
Zeros789
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:32:23.016677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1100
median599
Q33483.25
95-th percentile33000
Maximum37747126
Range37747126
Interquartile range (IQR)3383.25

Descriptive statistics

Standard deviation409174.18
Coefficient of variation (CV)20.870693
Kurtosis7246.086
Mean19605.203
Median Absolute Deviation (MAD)593
Skewness79.834802
Sum1.9605203 × 108
Variance1.6742351 × 1011
MonotonicityNot monotonic
2023-12-12T08:32:23.182436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 789
 
7.9%
11 112
 
1.1%
55 102
 
1.0%
110 91
 
0.9%
550 62
 
0.6%
220 56
 
0.6%
330 55
 
0.5%
22 52
 
0.5%
1 49
 
0.5%
12 48
 
0.5%
Other values (4113) 8584
85.8%
ValueCountFrequency (%)
0 789
7.9%
1 49
 
0.5%
2 23
 
0.2%
3 16
 
0.2%
4 17
 
0.2%
5 42
 
0.4%
6 15
 
0.1%
7 13
 
0.1%
8 20
 
0.2%
9 22
 
0.2%
ValueCountFrequency (%)
37747126 1
< 0.1%
6548228 1
< 0.1%
4808902 1
< 0.1%
4680999 1
< 0.1%
4589061 1
< 0.1%
4270545 1
< 0.1%
4252437 1
< 0.1%
3351227 1
< 0.1%
3308520 1
< 0.1%
3270671 1
< 0.1%

장소대분류
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
주거
2765 
기타
1787 
산업시설
1421 
자동차,철도차량
1232 
생활서비스
1001 
Other values (10)
1794 

Length

Max length9
Median length8
Mean length3.9298
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row산업시설
2nd row산업시설
3rd row기타
4th row의료,복지시설
5th row기타서비스

Common Values

ValueCountFrequency (%)
주거 2765
27.7%
기타 1787
17.9%
산업시설 1421
14.2%
자동차,철도차량 1232
12.3%
생활서비스 1001
 
10.0%
판매,업무시설 670
 
6.7%
기타서비스 489
 
4.9%
임야 287
 
2.9%
교육시설 86
 
0.9%
의료,복지시설 83
 
0.8%
Other values (5) 179
 
1.8%

Length

2023-12-12T08:32:23.348026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
주거 2765
27.7%
기타 1787
17.9%
산업시설 1421
14.2%
자동차,철도차량 1232
12.3%
생활서비스 1001
 
10.0%
판매,업무시설 670
 
6.7%
기타서비스 489
 
4.9%
임야 287
 
2.9%
교육시설 86
 
0.9%
의료,복지시설 83
 
0.8%
Other values (5) 179
 
1.8%

장소중분류
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
야외
1722 
단독주택
1405 
공동주택
1240 
자동차
1106 
음식점
704 
Other values (39)
3823 

Length

Max length7
Median length6
Mean length3.4864
Min length2

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row공장시설
2nd row창고시설
3rd row야외
4th row의료시설
5th row기타건축물

Common Values

ValueCountFrequency (%)
야외 1722
17.2%
단독주택 1405
14.1%
공동주택 1240
12.4%
자동차 1106
11.1%
음식점 704
7.0%
공장시설 639
 
6.4%
기타건축물 489
 
4.9%
창고시설 400
 
4.0%
일반업무 278
 
2.8%
판매시설 260
 
2.6%
Other values (34) 1757
17.6%

Length

2023-12-12T08:32:23.462509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
야외 1722
17.2%
단독주택 1405
14.1%
공동주택 1240
12.4%
자동차 1106
11.1%
음식점 704
7.0%
공장시설 639
 
6.4%
기타건축물 489
 
4.9%
창고시설 400
 
4.0%
일반업무 278
 
2.8%
판매시설 260
 
2.6%
Other values (34) 1757
17.6%

장소소분류
Text

Distinct257
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T08:32:23.761967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length22
Mean length4.8646
Min length1

Characters and Unicode

Total characters48646
Distinct characters285
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)0.4%

Sample

1st row그 밖의 공업
2nd row창고, 물품저장소
3rd row쓰레기
4th row한의원
5th row기타 건축물
ValueCountFrequency (%)
기타 1289
 
9.9%
단독주택 1017
 
7.8%
기타야외 757
 
5.8%
아파트 757
 
5.8%
쓰레기 711
 
5.4%
승용자동차 547
 
4.2%
건축물 472
 
3.6%
화물자동차 393
 
3.0%
창고 374
 
2.9%
한식 308
 
2.4%
Other values (283) 6432
49.3%
2023-12-12T08:32:24.384410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 3351
 
6.9%
3057
 
6.3%
주 2105
 
4.3%
타 2076
 
4.3%
택 1971
 
4.1%
물 1233
 
2.5%
동 1189
 
2.4%
자 1081
 
2.2%
차 1079
 
2.2%
단 1041
 
2.1%
Other values (275) 30463
62.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 44479
91.4%
Space Separator 3057
 
6.3%
Other Punctuation 618
 
1.3%
Open Punctuation 240
 
0.5%
Close Punctuation 240
 
0.5%
Uppercase Letter 12
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 3351
 
7.5%
주 2105
 
4.7%
타 2076
 
4.7%
택 1971
 
4.4%
물 1233
 
2.8%
동 1189
 
2.7%
자 1081
 
2.4%
차 1079
 
2.4%
단 1041
 
2.3%
독 1019
 
2.3%
Other values (266) 28334
63.7%
Uppercase Letter
ValueCountFrequency (%)
C 5
41.7%
P 5
41.7%
S 1
 
8.3%
A 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
, 617
99.8%
/ 1
 
0.2%
Space Separator
ValueCountFrequency (%)
3057
100.0%
Open Punctuation
ValueCountFrequency (%)
( 240
100.0%
Close Punctuation
ValueCountFrequency (%)
) 240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 44479
91.4%
Common 4155
 
8.5%
Latin 12
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 3351
 
7.5%
주 2105
 
4.7%
타 2076
 
4.7%
택 1971
 
4.4%
물 1233
 
2.8%
동 1189
 
2.7%
자 1081
 
2.4%
차 1079
 
2.4%
단 1041
 
2.3%
독 1019
 
2.3%
Other values (266) 28334
63.7%
Common
ValueCountFrequency (%)
3057
73.6%
, 617
 
14.8%
( 240
 
5.8%
) 240
 
5.8%
/ 1
 
< 0.1%
Latin
ValueCountFrequency (%)
C 5
41.7%
P 5
41.7%
S 1
 
8.3%
A 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 44479
91.4%
ASCII 4167
 
8.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 3351
 
7.5%
주 2105
 
4.7%
타 2076
 
4.7%
택 1971
 
4.4%
물 1233
 
2.8%
동 1189
 
2.7%
자 1081
 
2.4%
차 1079
 
2.4%
단 1041
 
2.3%
독 1019
 
2.3%
Other values (266) 28334
63.7%
ASCII
ValueCountFrequency (%)
3057
73.4%
, 617
 
14.8%
( 240
 
5.8%
) 240
 
5.8%
C 5
 
0.1%
P 5
 
0.1%
S 1
 
< 0.1%
/ 1
 
< 0.1%
A 1
 
< 0.1%

Interactions

2023-12-12T08:32:19.739545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:19.131580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:19.431227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:19.857011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:19.221920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:19.518969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:19.952082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:19.322430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:19.617850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:32:24.469189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도화재유형발화요인대분류발화요인소분류인명피해소계사망부상재산피해소계장소대분류장소중분류
시도1.0000.2040.1840.3620.0350.0000.0570.0000.2660.375
화재유형0.2041.0000.5190.6200.0000.0110.0000.0001.0001.000
발화요인대분류0.1840.5191.0001.0000.1430.1820.1460.1570.4180.507
발화요인소분류0.3620.6201.0001.0000.1830.1350.2000.2430.6020.585
인명피해소계0.0350.0000.1430.1831.0000.6750.9770.0370.0000.000
사망0.0000.0110.1820.1350.6751.0000.4580.0000.0760.039
부상0.0570.0000.1460.2000.9770.4581.0000.0730.0000.000
재산피해소계0.0000.0000.1570.2430.0370.0000.0731.0000.0520.000
장소대분류0.2661.0000.4180.6020.0000.0760.0000.0521.0001.000
장소중분류0.3751.0000.5070.5850.0000.0390.0000.0001.0001.000
2023-12-12T08:32:24.583887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
장소대분류발화요인소분류화재유형시도사망발화요인대분류장소중분류
장소대분류1.0000.2101.0000.0910.0430.1660.999
발화요인소분류0.2101.0000.3180.1030.0680.9980.132
화재유형1.0000.3181.0000.0970.0070.2290.998
시도0.0910.1030.0971.0000.0000.0670.109
사망0.0430.0680.0070.0001.0000.0850.019
발화요인대분류0.1660.9980.2290.0670.0851.0000.182
장소중분류0.9990.1320.9980.1090.0190.1821.000
2023-12-12T08:32:24.674062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인명피해소계부상재산피해소계시도화재유형발화요인대분류발화요인소분류사망장소대분류장소중분류
인명피해소계1.0000.9140.1530.0130.0000.0600.0630.4750.0000.000
부상0.9141.0000.1280.0230.0000.0620.0730.3100.0000.000
재산피해소계0.1530.1281.0000.0000.0000.0710.1210.0000.0230.000
시도0.0130.0230.0001.0000.0970.0670.1030.0000.0910.109
화재유형0.0000.0000.0000.0971.0000.2290.3180.0071.0000.998
발화요인대분류0.0600.0620.0710.0670.2291.0000.9980.0850.1660.182
발화요인소분류0.0630.0730.1210.1030.3180.9981.0000.0680.2100.132
사망0.4750.3100.0000.0000.0070.0850.0681.0000.0430.019
장소대분류0.0000.0000.0230.0911.0000.1660.2100.0431.0000.999
장소중분류0.0000.0000.0000.1090.9980.1820.1320.0190.9991.000

Missing values

2023-12-12T08:32:20.099242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:32:20.270994image/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

화재발생년월일시도시군구화재유형발화요인대분류발화요인소분류인명피해소계사망부상재산피해소계장소대분류장소중분류장소소분류
202212021-07-12 08:42경기도이천시건축,구조물화학적 요인화학적 발화(유증기 확산)000875산업시설공장시설그 밖의 공업
290082021-10-23 02:43경기도고양시덕양구건축,구조물전기적 요인미확인단락0001705산업시설창고시설창고, 물품저장소
245522021-08-26 18:59전라북도전주시완산구기타(쓰레기 화재등)부주의담배꽁초000108기타야외쓰레기
196002021-07-04 08:47부산광역시동래구건축,구조물자연적인 요인자연적 재해0000의료,복지시설의료시설한의원
309802021-11-12 07:43전라남도화순군건축,구조물부주의기기(전기, 기계 등) 사용.설치부주의000480기타서비스기타건축물기타 건축물
317492021-11-20 19:31경기도가평군건축,구조물부주의가연물 근접방치1012166주거단독주택단독주택
55282021-02-15 13:19강원도강릉시건축,구조물부주의쓰레기 소각000795산업시설창고시설기타 창고
142142021-05-01 23:08경상북도안동시기타(쓰레기 화재등)부주의가연물 근접방치0000기타야외쓰레기
22062021-01-17 00:10경기도하남시건축,구조물부주의음식물 조리중00013생활서비스음식점한식
177822021-06-11 12:36충청북도청주시 흥덕구기타(쓰레기 화재등)전기적 요인과부하/과전류0002505기타야외기타야외
화재발생년월일시도시군구화재유형발화요인대분류발화요인소분류인명피해소계사망부상재산피해소계장소대분류장소중분류장소소분류
164302021-05-26 20:54경상남도의령군건축,구조물부주의기타(부주의)0001680주거단독주택단독주택
212702021-07-22 22:11경기도수원시장안구건축,구조물부주의기기(전기, 기계 등) 사용.설치부주의0001116주거공동주택다세대주택
270922021-09-29 06:12울산광역시울주군기타(쓰레기 화재등)전기적 요인미확인단락00024기타야외기타야외
185962021-06-22 16:41경기도군포시기타(쓰레기 화재등)부주의담배꽁초000118기타야외쓰레기
120002021-04-14 03:06충청북도청주시 상당구건축,구조물전기적 요인미확인단락00064198판매,업무시설판매시설전통시장
79282021-03-06 23:52경상북도경산시건축,구조물전기적 요인절연열화에 의한 단락0001692주거공동주택아파트
111362021-04-06 20:53경기도용인시수지구기타(쓰레기 화재등)전기적 요인절연열화에 의한 단락000572기타야외기타야외
239552021-08-19 09:03경기도고양시일산서구건축,구조물전기적 요인미확인단락0004796교육시설연구,학원예체능학원
242582021-08-23 00:38충청남도아산시건축,구조물전기적 요인트래킹에 의한 단락000392주거단독주택단독주택
104232021-03-31 08:07서울특별시강서구건축,구조물전기적 요인누전,지락00041주거공동주택다세대주택