Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells11
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory157.0 B

Variable types

Numeric4
Categorical9
Text5

Dataset

Description○ 제공정보 : 장소별, 원인별, 년도별 등 화재통계를 실시간 검색하여 Excel 등으로 제공 ○ 제공시스템 : 국가화재정보시스템(www.nfds.go.kr), 재난안전데이터포털(data.mpss.go.kr)
Author소방청
URLhttps://www.data.go.kr/data/3038724/fileData.do

Alerts

발화요인소분류 is highly overall correlated with 발화열원 and 2 other fieldsHigh correlation
발화열원 is highly overall correlated with 발화열원소분류 and 2 other fieldsHigh correlation
장소중분류 is highly overall correlated with 장소대분류High correlation
발화요인대분류 is highly overall correlated with 발화열원 and 2 other fieldsHigh correlation
발화열원소분류 is highly overall correlated with 발화열원 and 2 other fieldsHigh correlation
장소대분류 is highly overall correlated with 장소중분류High correlation
부상 is highly overall correlated with 인명피해(명)소계High correlation
인명피해(명)소계 is highly overall correlated with 부상High correlation
사망 is highly imbalanced (97.2%)Imbalance
재산피해소계 is highly skewed (γ1 = 27.96248519)Skewed
연번 has unique valuesUnique
부상 has 9719 (97.2%) zerosZeros
인명피해(명)소계 has 9679 (96.8%) zerosZeros
재산피해소계 has 1386 (13.9%) zerosZeros

Reproduction

Analysis started2023-12-12 07:08:53.770586
Analysis finished2023-12-12 07:08:59.090033
Duration5.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22171.418
Minimum3
Maximum44433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:08:59.193167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile2213.75
Q111025.75
median21904.5
Q333434.25
95-th percentile42316.2
Maximum44433
Range44430
Interquartile range (IQR)22408.5

Descriptive statistics

Standard deviation12876.188
Coefficient of variation (CV)0.5807562
Kurtosis-1.2069886
Mean22171.418
Median Absolute Deviation (MAD)11164.5
Skewness0.018146798
Sum2.2171418 × 108
Variance1.6579623 × 108
MonotonicityNot monotonic
2023-12-12T16:08:59.343297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8333 1
 
< 0.1%
26660 1
 
< 0.1%
30754 1
 
< 0.1%
5837 1
 
< 0.1%
29311 1
 
< 0.1%
3692 1
 
< 0.1%
37447 1
 
< 0.1%
27389 1
 
< 0.1%
23523 1
 
< 0.1%
31864 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
3 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
9 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
21 1
< 0.1%
26 1
< 0.1%
38 1
< 0.1%
45 1
< 0.1%
ValueCountFrequency (%)
44433 1
< 0.1%
44432 1
< 0.1%
44431 1
< 0.1%
44430 1
< 0.1%
44429 1
< 0.1%
44425 1
< 0.1%
44417 1
< 0.1%
44415 1
< 0.1%
44394 1
< 0.1%
44393 1
< 0.1%

사망
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9953 
1
 
44
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 9953
99.5%
1 44
 
0.4%
2 3
 
< 0.1%

Length

2023-12-12T16:08:59.486390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:08:59.572385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9953
99.5%
1 44
 
0.4%
2 3
 
< 0.1%

부상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0395
Minimum0
Maximum9
Zeros9719
Zeros (%)97.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:08:59.664725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.29214429
Coefficient of variation (CV)7.3960579
Kurtosis254.43692
Mean0.0395
Median Absolute Deviation (MAD)0
Skewness13.139637
Sum395
Variance0.085348285
MonotonicityNot monotonic
2023-12-12T16:08:59.773024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 9719
97.2%
1 223
 
2.2%
2 32
 
0.3%
3 13
 
0.1%
4 7
 
0.1%
7 3
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 9719
97.2%
1 223
 
2.2%
2 32
 
0.3%
3 13
 
0.1%
4 7
 
0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 3
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
7 3
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
4 7
 
0.1%
3 13
 
0.1%
2 32
 
0.3%
1 223
 
2.2%
0 9719
97.2%

인명피해(명)소계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0445
Minimum0
Maximum9
Zeros9679
Zeros (%)96.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:08:59.900715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.30745602
Coefficient of variation (CV)6.909124
Kurtosis233.86
Mean0.0445
Median Absolute Deviation (MAD)0
Skewness12.514036
Sum445
Variance0.094529203
MonotonicityNot monotonic
2023-12-12T16:09:00.024228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 9679
96.8%
1 257
 
2.6%
2 38
 
0.4%
3 11
 
0.1%
4 8
 
0.1%
5 2
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 9679
96.8%
1 257
 
2.6%
2 38
 
0.4%
3 11
 
0.1%
4 8
 
0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 1
 
< 0.1%
7 2
 
< 0.1%
6 1
 
< 0.1%
5 2
 
< 0.1%
4 8
 
0.1%
3 11
 
0.1%
2 38
 
0.4%
1 257
 
2.6%
0 9679
96.8%

재산피해소계
Real number (ℝ)

SKEWED  ZEROS 

Distinct3602
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7827.4149
Minimum0
Maximum3131171
Zeros1386
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:09:00.175339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q149
median351
Q32466.25
95-th percentile23240.1
Maximum3131171
Range3131171
Interquartile range (IQR)2417.25

Descriptive statistics

Standard deviation71058.501
Coefficient of variation (CV)9.078157
Kurtosis954.53358
Mean7827.4149
Median Absolute Deviation (MAD)351
Skewness27.962485
Sum78274149
Variance5.0493106 × 109
MonotonicityNot monotonic
2023-12-12T16:09:00.327238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1386
 
13.9%
110 130
 
1.3%
55 103
 
1.0%
11 85
 
0.9%
330 81
 
0.8%
50 80
 
0.8%
30 75
 
0.8%
22 70
 
0.7%
220 66
 
0.7%
33 58
 
0.6%
Other values (3592) 7866
78.7%
ValueCountFrequency (%)
0 1386
13.9%
1 53
 
0.5%
2 6
 
0.1%
3 11
 
0.1%
4 6
 
0.1%
5 25
 
0.2%
6 11
 
0.1%
7 14
 
0.1%
8 23
 
0.2%
9 18
 
0.2%
ValueCountFrequency (%)
3131171 1
< 0.1%
2679031 1
< 0.1%
2462086 1
< 0.1%
2293195 1
< 0.1%
1951314 1
< 0.1%
1738750 1
< 0.1%
1305774 1
< 0.1%
1292414 1
< 0.1%
994441 1
< 0.1%
980878 1
< 0.1%

시도
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기도
2344 
서울특별시
1291 
경상남도
878 
경상북도
682 
충청남도
653 
Other values (12)
4152 

Length

Max length7
Median length5
Mean length4.0913
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시
2nd row경상북도
3rd row전라남도
4th row전라북도
5th row서울특별시

Common Values

ValueCountFrequency (%)
경기도 2344
23.4%
서울특별시 1291
12.9%
경상남도 878
 
8.8%
경상북도 682
 
6.8%
충청남도 653
 
6.5%
전라남도 625
 
6.2%
강원도 583
 
5.8%
전라북도 459
 
4.6%
부산광역시 445
 
4.5%
인천광역시 434
 
4.3%
Other values (7) 1606
16.1%

Length

2023-12-12T16:09:00.517756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 2344
23.4%
서울특별시 1291
12.9%
경상남도 878
 
8.8%
경상북도 682
 
6.8%
충청남도 653
 
6.5%
전라남도 625
 
6.2%
강원도 583
 
5.8%
전라북도 459
 
4.6%
부산광역시 445
 
4.5%
인천광역시 434
 
4.3%
Other values (7) 1606
16.1%

시군구
Text

Distinct229
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T16:09:00.831960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.3473
Min length2

Characters and Unicode

Total characters33473
Distinct characters142
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row구로구
2nd row고령군
3rd row담양군
4th row고창군
5th row강동구
ValueCountFrequency (%)
서구 251
 
2.5%
동구 202
 
2.0%
북구 200
 
2.0%
남구 178
 
1.8%
중구 166
 
1.6%
화성시 141
 
1.4%
안성시 130
 
1.3%
창원시 125
 
1.2%
남양주시 124
 
1.2%
강남구 117
 
1.2%
Other values (220) 8491
83.9%
2023-12-12T16:09:01.598469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 4944
 
14.8%
구 4570
 
13.7%
군 2088
 
6.2%
주 1318
 
3.9%
양 1012
 
3.0%
산 968
 
2.9%
천 961
 
2.9%
성 947
 
2.8%
남 847
 
2.5%
서 802
 
2.4%
Other values (132) 15016
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 33348
99.6%
Space Separator 125
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 4944
 
14.8%
구 4570
 
13.7%
군 2088
 
6.3%
주 1318
 
4.0%
양 1012
 
3.0%
산 968
 
2.9%
천 961
 
2.9%
성 947
 
2.8%
남 847
 
2.5%
서 802
 
2.4%
Other values (131) 14891
44.7%
Space Separator
ValueCountFrequency (%)
125
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 33348
99.6%
Common 125
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 4944
 
14.8%
구 4570
 
13.7%
군 2088
 
6.3%
주 1318
 
4.0%
양 1012
 
3.0%
산 968
 
2.9%
천 961
 
2.9%
성 947
 
2.8%
남 847
 
2.5%
서 802
 
2.4%
Other values (131) 14891
44.7%
Common
ValueCountFrequency (%)
125
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 33348
99.6%
ASCII 125
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 4944
 
14.8%
구 4570
 
13.7%
군 2088
 
6.3%
주 1318
 
4.0%
양 1012
 
3.0%
산 968
 
2.9%
천 961
 
2.9%
성 947
 
2.8%
남 847
 
2.5%
서 802
 
2.4%
Other values (131) 14891
44.7%
ASCII
ValueCountFrequency (%)
125
100.0%

읍면동
Text

Distinct2612
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T16:09:01.959782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0257
Min length2

Characters and Unicode

Total characters30257
Distinct characters342
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

Unique793 ?
Unique (%)7.9%

Sample

1st row구로동
2nd row대가야읍
3rd row무정면
4th row해리면
5th row고덕동
ValueCountFrequency (%)
남면 46
 
0.5%
신림동 35
 
0.4%
고잔동 32
 
0.3%
서면 31
 
0.3%
상계동 30
 
0.3%
상동 30
 
0.3%
신정동 30
 
0.3%
정왕동 29
 
0.3%
논현동 29
 
0.3%
안양동 28
 
0.3%
Other values (2602) 9680
96.8%
2023-12-12T16:09:02.512927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 6182
 
20.4%
면 2873
 
9.5%
읍 1334
 
4.4%
산 696
 
2.3%
신 452
 
1.5%
가 433
 
1.4%
성 421
 
1.4%
천 416
 
1.4%
정 396
 
1.3%
남 370
 
1.2%
Other values (332) 16684
55.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30023
99.2%
Decimal Number 234
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 6182
 
20.6%
면 2873
 
9.6%
읍 1334
 
4.4%
산 696
 
2.3%
신 452
 
1.5%
가 433
 
1.4%
성 421
 
1.4%
천 416
 
1.4%
정 396
 
1.3%
남 370
 
1.2%
Other values (325) 16450
54.8%
Decimal Number
ValueCountFrequency (%)
1 73
31.2%
2 73
31.2%
3 49
20.9%
4 16
 
6.8%
5 11
 
4.7%
7 7
 
3.0%
6 5
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30023
99.2%
Common 234
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 6182
 
20.6%
면 2873
 
9.6%
읍 1334
 
4.4%
산 696
 
2.3%
신 452
 
1.5%
가 433
 
1.4%
성 421
 
1.4%
천 416
 
1.4%
정 396
 
1.3%
남 370
 
1.2%
Other values (325) 16450
54.8%
Common
ValueCountFrequency (%)
1 73
31.2%
2 73
31.2%
3 49
20.9%
4 16
 
6.8%
5 11
 
4.7%
7 7
 
3.0%
6 5
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30023
99.2%
ASCII 234
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 6182
 
20.6%
면 2873
 
9.6%
읍 1334
 
4.4%
산 696
 
2.3%
신 452
 
1.5%
가 433
 
1.4%
성 421
 
1.4%
천 416
 
1.4%
정 396
 
1.3%
남 370
 
1.2%
Other values (325) 16450
54.8%
ASCII
ValueCountFrequency (%)
1 73
31.2%
2 73
31.2%
3 49
20.9%
4 16
 
6.8%
5 11
 
4.7%
7 7
 
3.0%
6 5
 
2.1%

발화열원
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
작동기기
3424 
담뱃불, 라이터불
2169 
불꽃, 불티
1995 
미상(발화원인)
1052 
마찰, 전도, 복사
900 
Other values (4)
460 

Length

Max length10
Median length9
Mean length6.6143
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row작동기기
2nd row작동기기
3rd row불꽃, 불티
4th row폭발물, 폭죽
5th row담뱃불, 라이터불

Common Values

ValueCountFrequency (%)
작동기기 3424
34.2%
담뱃불, 라이터불 2169
21.7%
불꽃, 불티 1995
20.0%
미상(발화원인) 1052
 
10.5%
마찰, 전도, 복사 900
 
9.0%
기타(발화원인) 320
 
3.2%
화학적 발화열 72
 
0.7%
자연적 발화열 58
 
0.6%
폭발물, 폭죽 10
 
0.1%

Length

2023-12-12T16:09:02.681973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:09:02.907914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
작동기기 3424
21.3%
담뱃불 2169
13.5%
라이터불 2169
13.5%
불꽃 1995
12.4%
불티 1995
12.4%
미상(발화원인 1052
 
6.5%
마찰 900
 
5.6%
전도 900
 
5.6%
복사 900
 
5.6%
기타(발화원인 320
 
2.0%
Other values (5) 280
 
1.7%

발화열원소분류
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
전기적 아크(단락)
1822 
담뱃불
1505 
미상
1052 
쓰레기, 논밭두렁
838 
기기 전도,복사열
769 
Other values (21)
4014 

Length

Max length12
Median length10
Mean length7.3518
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기기 전도,복사열
2nd row전기적 아크(단락)
3rd row쓰레기, 논밭두렁
4th row폭죽
5th row담뱃불

Common Values

ValueCountFrequency (%)
전기적 아크(단락) 1822
18.2%
담뱃불 1505
15.0%
미상 1052
10.5%
쓰레기, 논밭두렁 838
8.4%
기기 전도,복사열 769
7.7%
화염 전도,복사열 615
 
6.2%
라이터불, 성냥불 538
 
5.4%
불꽃, 스파크, 정전기 429
 
4.3%
기타(작동기기) 394
 
3.9%
기타(불꽃,불티) 349
 
3.5%
Other values (16) 1689
16.9%

Length

2023-12-12T16:09:03.107570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전기적 1822
 
10.8%
아크(단락 1822
 
10.8%
담뱃불 1505
 
9.0%
전도,복사열 1384
 
8.2%
미상 1052
 
6.3%
쓰레기 838
 
5.0%
논밭두렁 838
 
5.0%
기기 769
 
4.6%
스파크 635
 
3.8%
화염 615
 
3.7%
Other values (28) 5532
32.9%

발화요인대분류
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부주의
5328 
전기적 요인
1982 
기계적 요인
986 
미상
903 
방화의심
 
181
Other values (6)
620 

Length

Max length8
Median length3
Mean length3.877
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부주의 5328
53.3%
전기적 요인 1982
 
19.8%
기계적 요인 986
 
9.9%
미상 903
 
9.0%
방화의심 181
 
1.8%
기타 179
 
1.8%
교통사고 130
 
1.3%
방화 110
 
1.1%
화학적 요인 92
 
0.9%
자연적인 요인 74
 
0.7%

Length

2023-12-12T16:09:03.251565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부주의 5328
40.6%
요인 3134
23.9%
전기적 1982
 
15.1%
기계적 986
 
7.5%
미상 903
 
6.9%
방화의심 181
 
1.4%
기타 179
 
1.4%
교통사고 130
 
1.0%
방화 110
 
0.8%
화학적 92
 
0.7%
Other values (2) 109
 
0.8%

발화요인소분류
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
담배꽁초
1535 
음식물 조리중
993 
미상
903 
쓰레기 소각
779 
과열, 과부하
654 
Other values (39)
5136 

Length

Max length14
Median length11
Mean length6.3012
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row과열, 과부하
2nd row트래킹에 의한 단락
3rd row쓰레기 소각
4th row불씨,불꽃,화원방치
5th row담배꽁초

Common Values

ValueCountFrequency (%)
담배꽁초 1535
15.3%
음식물 조리중 993
 
9.9%
미상 903
 
9.0%
쓰레기 소각 779
 
7.8%
과열, 과부하 654
 
6.5%
불씨,불꽃,화원방치 648
 
6.5%
절연열화에 의한 단락 494
 
4.9%
미확인단락 484
 
4.8%
기타(부주의) 453
 
4.5%
가연물 근접방치 263
 
2.6%
Other values (34) 2794
27.9%

Length

2023-12-12T16:09:03.388355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
담배꽁초 1535
 
10.0%
의한 1011
 
6.6%
단락 1011
 
6.6%
음식물 993
 
6.5%
조리중 993
 
6.5%
미상 903
 
5.9%
쓰레기 779
 
5.1%
소각 779
 
5.1%
과열 654
 
4.3%
과부하 654
 
4.3%
Other values (50) 5997
39.2%

최초착화물대분류
Categorical

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
종이,목재,건초등
2694 
전기,전자
1701 
쓰레기류
1132 
합성수지
1037 
식품
840 
Other values (8)
2596 

Length

Max length15
Median length9
Mean length5.6621
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전기,전자
2nd row합성수지
3rd row종이,목재,건초등
4th row기타
5th row종이,목재,건초등

Common Values

ValueCountFrequency (%)
종이,목재,건초등 2694
26.9%
전기,전자 1701
17.0%
쓰레기류 1132
11.3%
합성수지 1037
 
10.4%
식품 840
 
8.4%
미상 720
 
7.2%
기타 662
 
6.6%
자동차,철도차량,선박,항공기 460
 
4.6%
침구,직물류 364
 
3.6%
위험물등 214
 
2.1%
Other values (3) 176
 
1.8%

Length

2023-12-12T16:09:03.549018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종이,목재,건초등 2694
26.9%
전기,전자 1701
17.0%
쓰레기류 1132
11.3%
합성수지 1037
 
10.4%
식품 840
 
8.4%
미상 720
 
7.2%
기타 662
 
6.6%
자동차,철도차량,선박,항공기 460
 
4.6%
침구,직물류 364
 
3.6%
위험물등 214
 
2.1%
Other values (3) 176
 
1.8%

최초착화물소분류
Text

Distinct78
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T16:09:03.885489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length5.2056
Min length2

Characters and Unicode

Total characters52056
Distinct characters151
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row전선피복
2nd row플라스틱, PVC, 비닐, 장판
3rd row목재, 합판
4th row기타
5th row종이
ValueCountFrequency (%)
전선피복 1204
 
8.6%
쓰레기 1069
 
7.6%
기타 868
 
6.2%
풀 769
 
5.5%
나뭇잎 769
 
5.5%
미상 720
 
5.1%
음식물 674
 
4.8%
종이 674
 
4.8%
플라스틱 669
 
4.8%
pvc 669
 
4.8%
Other values (83) 5990
42.6%
2023-12-12T16:09:04.330127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 4475
 
8.6%
4075
 
7.8%
기 3976
 
7.6%
전 2021
 
3.9%
타 1677
 
3.2%
선 1392
 
2.7%
이 1224
 
2.4%
피 1204
 
2.3%
복 1204
 
2.3%
판 1152
 
2.2%
Other values (141) 29656
57.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 39715
76.3%
Other Punctuation 4475
 
8.6%
Space Separator 4075
 
7.8%
Uppercase Letter 2007
 
3.9%
Open Punctuation 873
 
1.7%
Close Punctuation 873
 
1.7%
Decimal Number 38
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 3976
 
10.0%
전 2021
 
5.1%
타 1677
 
4.2%
선 1392
 
3.5%
이 1224
 
3.1%
피 1204
 
3.0%
복 1204
 
3.0%
판 1152
 
2.9%
레 1121
 
2.8%
스 1110
 
2.8%
Other values (131) 23634
59.5%
Uppercase Letter
ValueCountFrequency (%)
P 669
33.3%
C 669
33.3%
V 669
33.3%
Decimal Number
ValueCountFrequency (%)
4 34
89.5%
2 3
 
7.9%
5 1
 
2.6%
Other Punctuation
ValueCountFrequency (%)
, 4475
100.0%
Space Separator
ValueCountFrequency (%)
4075
100.0%
Open Punctuation
ValueCountFrequency (%)
( 873
100.0%
Close Punctuation
ValueCountFrequency (%)
) 873
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 39715
76.3%
Common 10334
 
19.9%
Latin 2007
 
3.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 3976
 
10.0%
전 2021
 
5.1%
타 1677
 
4.2%
선 1392
 
3.5%
이 1224
 
3.1%
피 1204
 
3.0%
복 1204
 
3.0%
판 1152
 
2.9%
레 1121
 
2.8%
스 1110
 
2.8%
Other values (131) 23634
59.5%
Common
ValueCountFrequency (%)
, 4475
43.3%
4075
39.4%
( 873
 
8.4%
) 873
 
8.4%
4 34
 
0.3%
2 3
 
< 0.1%
5 1
 
< 0.1%
Latin
ValueCountFrequency (%)
P 669
33.3%
C 669
33.3%
V 669
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 39715
76.3%
ASCII 12341
 
23.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 4475
36.3%
4075
33.0%
( 873
 
7.1%
) 873
 
7.1%
P 669
 
5.4%
C 669
 
5.4%
V 669
 
5.4%
4 34
 
0.3%
2 3
 
< 0.1%
5 1
 
< 0.1%
Hangul
ValueCountFrequency (%)
기 3976
 
10.0%
전 2021
 
5.1%
타 1677
 
4.2%
선 1392
 
3.5%
이 1224
 
3.1%
피 1204
 
3.0%
복 1204
 
3.0%
판 1152
 
2.9%
레 1121
 
2.8%
스 1110
 
2.8%
Other values (131) 23634
59.5%

장소대분류
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
주거
2639 
기타
2108 
산업시설
1126 
자동차,철도차량
1118 
생활서비스
1005 
Other values (9)
2004 

Length

Max length9
Median length2
Mean length3.6921
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row산업시설
2nd row주거
3rd row기타
4th row기타
5th row주거

Common Values

ValueCountFrequency (%)
주거 2639
26.4%
기타 2108
21.1%
산업시설 1126
11.3%
자동차,철도차량 1118
11.2%
생활서비스 1005
 
10.1%
임야 753
 
7.5%
판매,업무시설 513
 
5.1%
기타서비스 431
 
4.3%
집합시설 76
 
0.8%
의료,복지시설 70
 
0.7%
Other values (4) 161
 
1.6%

Length

2023-12-12T16:09:04.509568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
주거 2639
26.4%
기타 2108
21.1%
산업시설 1126
11.3%
자동차,철도차량 1118
11.2%
생활서비스 1005
 
10.1%
임야 753
 
7.5%
판매,업무시설 513
 
5.1%
기타서비스 431
 
4.3%
집합시설 76
 
0.8%
의료,복지시설 70
 
0.7%
Other values (4) 161
 
1.6%

장소중분류
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
야외
2033 
단독주택
1425 
공동주택
1108 
자동차
1025 
음식점
615 
Other values (39)
3794 

Length

Max length7
Median length6
Mean length3.3452
Min length2

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row작업장
2nd row단독주택
3rd row야외
4th row야외
5th row공동주택

Common Values

ValueCountFrequency (%)
야외 2033
20.3%
단독주택 1425
14.2%
공동주택 1108
11.1%
자동차 1025
10.2%
음식점 615
 
6.2%
공장시설 541
 
5.4%
기타건축물 431
 
4.3%
산불 401
 
4.0%
들불 352
 
3.5%
일상서비스 275
 
2.8%
Other values (34) 1794
17.9%

Length

2023-12-12T16:09:04.636735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
야외 2033
20.3%
단독주택 1425
14.2%
공동주택 1108
11.1%
자동차 1025
10.2%
음식점 615
 
6.2%
공장시설 541
 
5.4%
기타건축물 431
 
4.3%
산불 401
 
4.0%
들불 352
 
3.5%
일상서비스 275
 
2.8%
Other values (34) 1794
17.9%

장소소분류
Text

Distinct247
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T16:09:04.995837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length4.6674
Min length1

Characters and Unicode

Total characters46674
Distinct characters266
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

Unique51 ?
Unique (%)0.5%

Sample

1st row기타 작업장
2nd row단독주택
3rd row기타야외
4th row공터
5th row아파트
ValueCountFrequency (%)
기타 1269
 
10.0%
단독주택 1091
 
8.6%
기타야외 1008
 
7.9%
쓰레기 689
 
5.4%
아파트 669
 
5.3%
승용자동차 485
 
3.8%
건축물 414
 
3.3%
화물자동차 368
 
2.9%
사유림 318
 
2.5%
다가구주택 262
 
2.1%
Other values (272) 6147
48.3%
2023-12-12T16:09:05.463973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 3501
 
7.5%
2720
 
5.8%
타 2305
 
4.9%
주 2080
 
4.5%
택 1949
 
4.2%
야 1121
 
2.4%
단 1117
 
2.4%
독 1091
 
2.3%
동 1066
 
2.3%
외 1014
 
2.2%
Other values (256) 28710
61.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43062
92.3%
Space Separator 2720
 
5.8%
Other Punctuation 460
 
1.0%
Close Punctuation 191
 
0.4%
Open Punctuation 191
 
0.4%
Uppercase Letter 50
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 3501
 
8.1%
타 2305
 
5.4%
주 2080
 
4.8%
택 1949
 
4.5%
야 1121
 
2.6%
단 1117
 
2.6%
독 1091
 
2.5%
동 1066
 
2.5%
외 1014
 
2.4%
차 1004
 
2.3%
Other values (247) 26814
62.3%
Uppercase Letter
ValueCountFrequency (%)
P 17
34.0%
C 17
34.0%
S 8
16.0%
A 8
16.0%
Other Punctuation
ValueCountFrequency (%)
, 452
98.3%
/ 8
 
1.7%
Space Separator
ValueCountFrequency (%)
2720
100.0%
Close Punctuation
ValueCountFrequency (%)
) 191
100.0%
Open Punctuation
ValueCountFrequency (%)
( 191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 43062
92.3%
Common 3562
 
7.6%
Latin 50
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 3501
 
8.1%
타 2305
 
5.4%
주 2080
 
4.8%
택 1949
 
4.5%
야 1121
 
2.6%
단 1117
 
2.6%
독 1091
 
2.5%
동 1066
 
2.5%
외 1014
 
2.4%
차 1004
 
2.3%
Other values (247) 26814
62.3%
Common
ValueCountFrequency (%)
2720
76.4%
, 452
 
12.7%
) 191
 
5.4%
( 191
 
5.4%
/ 8
 
0.2%
Latin
ValueCountFrequency (%)
P 17
34.0%
C 17
34.0%
S 8
16.0%
A 8
16.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 43062
92.3%
ASCII 3612
 
7.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 3501
 
8.1%
타 2305
 
5.4%
주 2080
 
4.8%
택 1949
 
4.5%
야 1121
 
2.6%
단 1117
 
2.6%
독 1091
 
2.5%
동 1066
 
2.5%
외 1014
 
2.4%
차 1004
 
2.3%
Other values (247) 26814
62.3%
ASCII
ValueCountFrequency (%)
2720
75.3%
, 452
 
12.5%
) 191
 
5.3%
( 191
 
5.3%
P 17
 
0.5%
C 17
 
0.5%
S 8
 
0.2%
/ 8
 
0.2%
A 8
 
0.2%

지번동
Text

Distinct3218
Distinct (%)32.2%
Missing11
Missing (%)0.1%
Memory size156.2 KiB
2023-12-12T16:09:05.814487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length3.2335569
Min length2

Characters and Unicode

Total characters32300
Distinct characters346
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

Unique1103 ?
Unique (%)11.0%

Sample

1st row구로2동
2nd row대가야읍
3rd row무정면
4th row해리면
5th row고덕2동
ValueCountFrequency (%)
남면 46
 
0.5%
서면 31
 
0.3%
신림동 29
 
0.3%
고잔동 25
 
0.3%
동면 24
 
0.2%
공도읍 23
 
0.2%
진접읍 22
 
0.2%
상동 22
 
0.2%
팔탄면 22
 
0.2%
화도읍 22
 
0.2%
Other values (3208) 9723
97.3%
2023-12-12T16:09:06.365212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 6175
 
19.1%
면 2869
 
8.9%
읍 1337
 
4.1%
1 844
 
2.6%
2 782
 
2.4%
산 692
 
2.1%
신 442
 
1.4%
가 433
 
1.3%
성 426
 
1.3%
천 413
 
1.3%
Other values (336) 17887
55.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30010
92.9%
Decimal Number 2258
 
7.0%
Other Punctuation 32
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 6175
 
20.6%
면 2869
 
9.6%
읍 1337
 
4.5%
산 692
 
2.3%
신 442
 
1.5%
가 433
 
1.4%
성 426
 
1.4%
천 413
 
1.4%
정 395
 
1.3%
남 366
 
1.2%
Other values (324) 16462
54.9%
Decimal Number
ValueCountFrequency (%)
1 844
37.4%
2 782
34.6%
3 326
 
14.4%
4 142
 
6.3%
5 69
 
3.1%
6 36
 
1.6%
7 35
 
1.6%
8 13
 
0.6%
9 10
 
0.4%
0 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 21
65.6%
, 11
34.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30010
92.9%
Common 2290
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 6175
 
20.6%
면 2869
 
9.6%
읍 1337
 
4.5%
산 692
 
2.3%
신 442
 
1.5%
가 433
 
1.4%
성 426
 
1.4%
천 413
 
1.4%
정 395
 
1.3%
남 366
 
1.2%
Other values (324) 16462
54.9%
Common
ValueCountFrequency (%)
1 844
36.9%
2 782
34.1%
3 326
 
14.2%
4 142
 
6.2%
5 69
 
3.0%
6 36
 
1.6%
7 35
 
1.5%
. 21
 
0.9%
8 13
 
0.6%
, 11
 
0.5%
Other values (2) 11
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30010
92.9%
ASCII 2290
 
7.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 6175
 
20.6%
면 2869
 
9.6%
읍 1337
 
4.5%
산 692
 
2.3%
신 442
 
1.5%
가 433
 
1.4%
성 426
 
1.4%
천 413
 
1.4%
정 395
 
1.3%
남 366
 
1.2%
Other values (324) 16462
54.9%
ASCII
ValueCountFrequency (%)
1 844
36.9%
2 782
34.1%
3 326
 
14.2%
4 142
 
6.2%
5 69
 
3.0%
6 36
 
1.6%
7 35
 
1.5%
. 21
 
0.9%
8 13
 
0.6%
, 11
 
0.5%
Other values (2) 11
 
0.5%

Interactions

2023-12-12T16:08:58.231267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:56.954627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:57.440330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:57.839017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:58.315341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:57.096635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:57.554916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:57.935688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:58.412333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:57.213928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:57.641429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:58.020652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:58.527668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:57.333419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:57.739093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:58.133606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:09:06.470603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번사망부상인명피해(명)소계재산피해소계시도발화열원발화열원소분류발화요인대분류발화요인소분류최초착화물대분류최초착화물소분류장소대분류장소중분류
연번1.0000.0000.0000.0000.0050.1090.1760.2580.1400.2770.1920.2920.1830.223
사망0.0001.0000.2010.5040.1800.0530.1190.0910.1380.1500.1490.2790.0900.065
부상0.0000.2011.0000.9780.2530.0570.0590.1060.1660.2750.1920.2600.1170.214
인명피해(명)소계0.0000.5040.9781.0000.4070.0650.0580.1110.1880.2910.2000.3000.1270.219
재산피해소계0.0050.1800.2530.4071.0000.0000.0480.0000.0680.0000.0590.0000.2290.265
시도0.1090.0530.0570.0650.0001.0000.2650.3370.1480.3420.2610.3620.2820.386
발화열원0.1760.1190.0590.0580.0480.2651.0001.0000.8410.9480.7010.7950.4060.503
발화열원소분류0.2580.0910.1060.1110.0000.3371.0001.0000.8990.9530.8310.8620.5470.579
발화요인대분류0.1400.1380.1660.1880.0680.1480.8410.8991.0001.0000.7410.8420.4350.571
발화요인소분류0.2770.1500.2750.2910.0000.3420.9480.9531.0001.0000.8690.8900.6360.700
최초착화물대분류0.1920.1490.1920.2000.0590.2610.7010.8310.7410.8691.0001.0000.5800.669
최초착화물소분류0.2920.2790.2600.3000.0000.3620.7950.8620.8420.8901.0001.0000.7310.738
장소대분류0.1830.0900.1170.1270.2290.2820.4060.5470.4350.6360.5800.7311.0001.000
장소중분류0.2230.0650.2140.2190.2650.3860.5030.5790.5710.7000.6690.7381.0001.000
2023-12-12T16:09:06.635241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발화요인소분류발화열원장소중분류최초착화물대분류사망발화요인대분류발화열원소분류장소대분류시도
발화요인소분류1.0000.7270.1470.4810.0730.9980.5590.2380.098
발화열원0.7271.0000.2050.3940.0520.5930.9990.1840.109
장소중분류0.1470.2051.0000.2680.0320.2080.1590.9980.113
최초착화물대분류0.4810.3940.2681.0000.0840.4120.3860.2520.094
사망0.0730.0520.0320.0841.0000.0800.0460.0490.028
발화요인대분류0.9980.5930.2080.4120.0801.0000.5930.1860.056
발화열원소분류0.5590.9990.1590.3860.0460.5931.0000.2010.103
장소대분류0.2380.1840.9980.2520.0490.1860.2011.0000.100
시도0.0980.1090.1130.0940.0280.0560.1030.1001.000
2023-12-12T16:09:06.757987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번부상인명피해(명)소계재산피해소계사망시도발화열원발화열원소분류발화요인대분류발화요인소분류최초착화물대분류장소대분류장소중분류
연번1.0000.002-0.0020.0260.0000.0420.0810.0960.0600.0980.0800.0750.078
부상0.0021.0000.9340.1130.0900.0230.0190.0400.0750.1020.0830.0490.078
인명피해(명)소계-0.0020.9341.0000.1330.3500.0250.0260.0400.0810.1040.0840.0510.077
재산피해소계0.0260.1130.1331.0000.1080.0000.0220.0000.0290.0000.0240.0940.094
사망0.0000.0900.3500.1081.0000.0280.0520.0460.0800.0730.0840.0490.032
시도0.0420.0230.0250.0000.0281.0000.1090.1030.0560.0980.0940.1000.113
발화열원0.0810.0190.0260.0220.0520.1091.0000.9990.5930.7270.3940.1840.205
발화열원소분류0.0960.0400.0400.0000.0460.1030.9991.0000.5930.5590.3860.2010.159
발화요인대분류0.0600.0750.0810.0290.0800.0560.5930.5931.0000.9980.4120.1860.208
발화요인소분류0.0980.1020.1040.0000.0730.0980.7270.5590.9981.0000.4810.2380.147
최초착화물대분류0.0800.0830.0840.0240.0840.0940.3940.3860.4120.4811.0000.2520.268
장소대분류0.0750.0490.0510.0940.0490.1000.1840.2010.1860.2380.2521.0000.998
장소중분류0.0780.0780.0770.0940.0320.1130.2050.1590.2080.1470.2680.9981.000

Missing values

2023-12-12T16:08:58.719310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:08:58.977656image/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

연번사망부상인명피해(명)소계재산피해소계시도시군구읍면동발화열원발화열원소분류발화요인대분류발화요인소분류최초착화물대분류최초착화물소분류장소대분류장소중분류장소소분류지번동
833283330002031서울특별시구로구구로동작동기기기기 전도,복사열기계적 요인과열, 과부하전기,전자전선피복산업시설작업장기타 작업장구로2동
316043160500078경상북도고령군대가야읍작동기기전기적 아크(단락)전기적 요인트래킹에 의한 단락합성수지플라스틱, PVC, 비닐, 장판주거단독주택단독주택대가야읍
2748227483000110전라남도담양군무정면불꽃, 불티쓰레기, 논밭두렁부주의쓰레기 소각종이,목재,건초등목재, 합판기타야외기타야외무정면
201802018100075전라북도고창군해리면폭발물, 폭죽폭죽부주의불씨,불꽃,화원방치기타기타기타야외공터해리면
4291429200039서울특별시강동구고덕동담뱃불, 라이터불담뱃불부주의담배꽁초종이,목재,건초등종이주거공동주택아파트고덕2동
1736417365000880대구광역시서구중리동작동기기불꽃, 스파크, 정전기기계적 요인기타(기계적요인)쓰레기류기타 쓰레기산업시설공장시설그 밖의 공업중리동
12187121880000대구광역시서구평리동마찰, 전도, 복사화염 전도,복사열부주의음식물 조리중식품음식물주거단독주택다가구주택평리6동
38122381230001672경기도김포시사우동마찰, 전도, 복사기타(마찰,전도,복사)기타기타종이,목재,건초등목재, 합판주거단독주택단독주택사우동
300293003001187전라남도영암군삼호읍미상(발화원인)미상부주의음식물 조리중종이,목재,건초등목재, 합판주거단독주택단독주택삼호읍
23292232930001815서울특별시영등포구여의도동작동기기전기적 아크(단락)전기적 요인절연열화에 의한 단락전기,전자전선피복판매,업무시설일반업무일반회사여의도동
연번사망부상인명피해(명)소계재산피해소계시도시군구읍면동발화열원발화열원소분류발화요인대분류발화요인소분류최초착화물대분류최초착화물소분류장소대분류장소중분류장소소분류지번동
53915392000343서울특별시동대문구장안동마찰, 전도, 복사화염 전도,복사열부주의기타(부주의)쓰레기류분진산업시설작업장기타 작업장장안1동
522952300000부산광역시중구남포동5가작동기기불꽃, 스파크, 정전기전기적 요인접촉불량에 의한 단락전기,전자전기, 전자기기 케이스생활서비스음식점횟집남포동5가
3658736588000116경상북도경주시안강읍불꽃, 불티굴뚝(연통) 아궁이부주의불씨,불꽃,화원방치합성수지플라스틱, PVC, 비닐, 장판기타서비스기타건축물기타 건축물안강읍
32188321890005197경기도동두천시하봉암동담뱃불, 라이터불담뱃불부주의담배꽁초쓰레기류기타 쓰레기산업시설공장시설금속기계 및 기구공업하봉암동
7337340009401광주광역시서구매월동작동기기전기적 아크(단락)전기적 요인압착,손상에 의한 단락전기,전자전선피복판매,업무시설판매시설상가빌딩매월동
15545155460001434강원도화천군화천읍작동기기기타(작동기기)기계적 요인과열, 과부하전기,전자전자기기 부속품기타서비스기타건축물기타 건축물화천읍
3929039291000125부산광역시서구남부민동작동기기기기 전도,복사열부주의기타(부주의)침구,직물류기타(침구,직물류)주거단독주택단독주택남부민2동
24836248370000전라남도목포시대양동미상(발화원인)미상미상미상쓰레기류쓰레기기타야외쓰레기대양동
2200222003000492전라남도곡성군석곡면미상(발화원인)미상미상미상미상미상생활서비스음식점한식석곡면
1405914060000837경상남도함양군안의면불꽃, 불티쓰레기, 논밭두렁부주의쓰레기 소각종이,목재,건초등건초주거기타주택기타 주택안의면