Overview

Dataset statistics

Number of variables15
Number of observations937
Missing cells114
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory115.4 KiB
Average record size in memory126.1 B

Variable types

DateTime1
Numeric4
Categorical10

Dataset

Description대전광역시 유성구 화재발생 현황에 대한 데이터로 발생일시, 사상자수, 관할소방서, 화재유형, 피해액, 시군구코드, 행정동코드 등의 항목을 제공합니다.
Author대전광역시 유성구
URLhttps://www.data.go.kr/data/15111193/fileData.do

Alerts

시도코드 has constant value ""Constant
시도이름 has constant value ""Constant
시군구코드 has constant value ""Constant
시군구이름 has constant value ""Constant
발화요인 is highly overall correlated with 화재유형High correlation
화재유형 is highly overall correlated with 발화요인High correlation
법정동이름 is highly overall correlated with 행정동코드 and 3 other fieldsHigh correlation
관할소방서 is highly overall correlated with 행정동코드 and 2 other fieldsHigh correlation
행정동코드 is highly overall correlated with 법정동코드 and 3 other fieldsHigh correlation
법정동코드 is highly overall correlated with 행정동코드 and 1 other fieldsHigh correlation
행정동이름 is highly overall correlated with 행정동코드 and 2 other fieldsHigh correlation
피해액 has 114 (12.2%) missing valuesMissing
발생일시 has unique valuesUnique
사상자수 has 906 (96.7%) zerosZeros

Reproduction

Analysis started2023-12-12 14:08:54.321187
Analysis finished2023-12-12 14:08:57.452372
Duration3.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

발생일시
Date

UNIQUE 

Distinct937
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
Minimum2016-01-02 09:21:00
Maximum2021-01-09 10:48:00
2023-12-12T23:08:57.514918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:57.646282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사상자수
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.060832444
Minimum0
Maximum9
Zeros906
Zeros (%)96.7%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2023-12-12T23:08:57.759204image/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 deviation0.47763277
Coefficient of variation (CV)7.8516124
Kurtosis205.87835
Mean0.060832444
Median Absolute Deviation (MAD)0
Skewness13.145207
Sum57
Variance0.22813307
MonotonicityNot monotonic
2023-12-12T23:08:57.853686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 906
96.7%
1 22
 
2.3%
2 5
 
0.5%
6 1
 
0.1%
7 1
 
0.1%
9 1
 
0.1%
3 1
 
0.1%
ValueCountFrequency (%)
0 906
96.7%
1 22
 
2.3%
2 5
 
0.5%
3 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
9 1
 
0.1%
ValueCountFrequency (%)
9 1
 
0.1%
7 1
 
0.1%
6 1
 
0.1%
3 1
 
0.1%
2 5
 
0.5%
1 22
 
2.3%
0 906
96.7%

관할소방서
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
구암 119안전센터
303 
궁동 119안전센터
181 
전민 119안전센터
172 
노은 119안전센터
151 
도룡 119안전센터
130 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row노은 119안전센터
2nd row구암 119안전센터
3rd row도룡 119안전센터
4th row구암 119안전센터
5th row구암 119안전센터

Common Values

ValueCountFrequency (%)
구암 119안전센터 303
32.3%
궁동 119안전센터 181
19.3%
전민 119안전센터 172
18.4%
노은 119안전센터 151
16.1%
도룡 119안전센터 130
13.9%

Length

2023-12-12T23:08:57.965075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:08:58.047509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
119안전센터 937
50.0%
구암 303
 
16.2%
궁동 181
 
9.7%
전민 172
 
9.2%
노은 151
 
8.1%
도룡 130
 
6.9%

화재유형
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
부주의
488 
전기적 요인
290 
기계적 요인
 
45
미상
 
39
화학적 요인
 
18
Other values (7)
57 

Length

Max length8
Median length3
Mean length4.1141942
Min length2

Unique

Unique2 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
부주의 488
52.1%
전기적 요인 290
30.9%
기계적 요인 45
 
4.8%
미상 39
 
4.2%
화학적 요인 18
 
1.9%
방화 14
 
1.5%
방화의심 14
 
1.5%
교통사고 14
 
1.5%
기타 10
 
1.1%
가스누출(폭발) 3
 
0.3%
Other values (2) 2
 
0.2%

Length

2023-12-12T23:08:58.176887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부주의 488
37.8%
요인 354
27.4%
전기적 290
22.5%
기계적 45
 
3.5%
미상 39
 
3.0%
화학적 18
 
1.4%
방화 14
 
1.1%
방화의심 14
 
1.1%
교통사고 14
 
1.1%
기타 10
 
0.8%
Other values (3) 5
 
0.4%

피해액
Real number (ℝ)

MISSING 

Distinct638
Distinct (%)77.5%
Missing114
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean5470.8481
Minimum9
Maximum686656
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2023-12-12T23:08:58.306821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile42
Q1171.5
median735
Q32857
95-th percentile19744.1
Maximum686656
Range686647
Interquartile range (IQR)2685.5

Descriptive statistics

Standard deviation28604.907
Coefficient of variation (CV)5.2286055
Kurtosis400.12007
Mean5470.8481
Median Absolute Deviation (MAD)655
Skewness17.919527
Sum4502508
Variance8.1824069 × 108
MonotonicityNot monotonic
2023-12-12T23:08:58.421328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
220 11
 
1.2%
110 9
 
1.0%
88 5
 
0.5%
2200 5
 
0.5%
440 5
 
0.5%
1100 5
 
0.5%
50 4
 
0.4%
80 4
 
0.4%
121 4
 
0.4%
60 4
 
0.4%
Other values (628) 767
81.9%
(Missing) 114
 
12.2%
ValueCountFrequency (%)
9 1
 
0.1%
12 1
 
0.1%
15 2
0.2%
17 3
0.3%
18 3
0.3%
19 3
0.3%
20 1
 
0.1%
22 2
0.2%
23 4
0.4%
25 1
 
0.1%
ValueCountFrequency (%)
686656 1
0.1%
235517 1
0.1%
185967 1
0.1%
149890 1
0.1%
124325 1
0.1%
115187 1
0.1%
100100 1
0.1%
99004 1
0.1%
94117 1
0.1%
82077 1
0.1%

발화요인
Categorical

HIGH CORRELATION 

Distinct42
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
담배꽁초
203 
절연열화에 의한 단락
133 
음식물 조리중
75 
불씨.불꽃.화원방치
53 
기타(부주의)
48 
Other values (37)
425 

Length

Max length21
Median length12
Mean length6.8708645
Min length2

Unique

Unique8 ?
Unique (%)0.9%

Sample

1st row반단선
2nd row미상
3rd row미상
4th row담배꽁초
5th row쓰레기 소각

Common Values

ValueCountFrequency (%)
담배꽁초 203
21.7%
절연열화에 의한 단락 133
14.2%
음식물 조리중 75
 
8.0%
불씨.불꽃.화원방치 53
 
5.7%
기타(부주의) 48
 
5.1%
쓰레기 소각 40
 
4.3%
미상 39
 
4.2%
트래킹에 의한 단락 30
 
3.2%
과열. 과부하 30
 
3.2%
과부하/과전류 28
 
3.0%
Other values (32) 258
27.5%

Length

2023-12-12T23:08:58.556270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
담배꽁초 203
13.1%
단락 190
 
12.2%
의한 190
 
12.2%
절연열화에 133
 
8.6%
음식물 75
 
4.8%
조리중 75
 
4.8%
불씨.불꽃.화원방치 53
 
3.4%
기타(부주의 48
 
3.1%
쓰레기 40
 
2.6%
소각 40
 
2.6%
Other values (50) 506
32.6%

장소유형
Categorical

Distinct12
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
주거
248 
기타
166 
생활서비스
163 
자동차.철도차량
127 
산업시설
63 
Other values (7)
170 

Length

Max length8
Median length2
Mean length3.9188901
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row주거
2nd row주거
3rd row주거
4th row주거
5th row임야

Common Values

ValueCountFrequency (%)
주거 248
26.5%
기타 166
17.7%
생활서비스 163
17.4%
자동차.철도차량 127
13.6%
산업시설 63
 
6.7%
임야 55
 
5.9%
판매.업무시설 40
 
4.3%
교육시설 30
 
3.2%
기타서비스 23
 
2.5%
의료.복지시설 10
 
1.1%
Other values (2) 12
 
1.3%

Length

2023-12-12T23:08:58.657255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
주거 248
26.5%
기타 166
17.7%
생활서비스 163
17.4%
자동차.철도차량 127
13.6%
산업시설 63
 
6.7%
임야 55
 
5.9%
판매.업무시설 40
 
4.3%
교육시설 30
 
3.2%
기타서비스 23
 
2.5%
의료.복지시설 10
 
1.1%
Other values (2) 12
 
1.3%

시도코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
3000000000
937 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3000000000
2nd row3000000000
3rd row3000000000
4th row3000000000
5th row3000000000

Common Values

ValueCountFrequency (%)
3000000000 937
100.0%

Length

2023-12-12T23:08:58.759967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:08:58.836318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3000000000 937
100.0%

시도이름
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
대전광역시
937 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 937
100.0%

Length

2023-12-12T23:08:58.915644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:08:59.010050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 937
100.0%

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
3020000000
937 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3020000000
2nd row3020000000
3rd row3020000000
4th row3020000000
5th row3020000000

Common Values

ValueCountFrequency (%)
3020000000 937
100.0%

Length

2023-12-12T23:08:59.132601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:08:59.231286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3020000000 937
100.0%

시군구이름
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
유성구
937 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
유성구 937
100.0%

Length

2023-12-12T23:08:59.332777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:08:59.413987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유성구 937
100.0%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0200553 × 109
Minimum3.0200526 × 109
Maximum3.020061 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2023-12-12T23:08:59.502907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0200526 × 109
5-th percentile3.0200527 × 109
Q13.020053 × 109
median3.0200546 × 109
Q33.020057 × 109
95-th percentile3.02006 × 109
Maximum3.020061 × 109
Range8400
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation2531.4462
Coefficient of variation (CV)8.3821187 × 10-7
Kurtosis-0.18437454
Mean3.0200553 × 109
Median Absolute Deviation (MAD)1600
Skewness1.0383907
Sum2.8297918 × 1012
Variance6408219.8
MonotonicityNot monotonic
2023-12-12T23:08:59.629435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3020053000 170
18.1%
3020054000 140
14.9%
3020055000 111
11.8%
3020060000 99
10.6%
3020054600 79
8.4%
3020058000 54
 
5.8%
3020057000 51
 
5.4%
3020054800 50
 
5.3%
3020054700 50
 
5.3%
3020052600 45
 
4.8%
Other values (2) 88
9.4%
ValueCountFrequency (%)
3020052600 45
 
4.8%
3020052700 44
 
4.7%
3020053000 170
18.1%
3020054000 140
14.9%
3020054600 79
8.4%
3020054700 50
 
5.3%
3020054800 50
 
5.3%
3020055000 111
11.8%
3020057000 51
 
5.4%
3020058000 54
 
5.8%
ValueCountFrequency (%)
3020061000 44
 
4.7%
3020060000 99
10.6%
3020058000 54
 
5.8%
3020057000 51
 
5.4%
3020055000 111
11.8%
3020054800 50
 
5.3%
3020054700 50
 
5.3%
3020054600 79
8.4%
3020054000 140
14.9%
3020053000 170
18.1%

행정동이름
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
온천1동
170 
온천2동
140 
신성동
111 
관평동
99 
노은1동
79 
Other values (7)
338 

Length

Max length4
Median length4
Mean length3.5688367
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row노은3동
2nd row온천1동
3rd row신성동
4th row온천1동
5th row온천1동

Common Values

ValueCountFrequency (%)
온천1동 170
18.1%
온천2동 140
14.9%
신성동 111
11.8%
관평동 99
10.6%
노은1동 79
8.4%
구즉동 54
 
5.8%
전민동 51
 
5.4%
노은3동 50
 
5.3%
노은2동 50
 
5.3%
학하동 45
 
4.8%
Other values (2) 88
9.4%

Length

2023-12-12T23:08:59.827540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
온천1동 170
18.1%
온천2동 140
14.9%
신성동 111
11.8%
관평동 99
10.6%
노은1동 79
8.4%
구즉동 54
 
5.8%
전민동 51
 
5.4%
노은3동 50
 
5.3%
노은2동 50
 
5.3%
학하동 45
 
4.8%
Other values (2) 88
9.4%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0200573 × 109
Minimum3.0200105 × 109
Maximum3.0230118 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2023-12-12T23:09:00.052334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0200105 × 109
5-th percentile3.0200111 × 109
Q13.0200114 × 109
median3.020012 × 109
Q33.0200139 × 109
95-th percentile3.0200147 × 109
Maximum3.0230118 × 109
Range3001300
Interquartile range (IQR)2500

Descriptive statistics

Standard deviation364068.98
Coefficient of variation (CV)0.00012055036
Kurtosis62.280412
Mean3.0200573 × 109
Median Absolute Deviation (MAD)800
Skewness8.0091609
Sum2.8297937 × 1012
Variance1.3254622 × 1011
MonotonicityNot monotonic
2023-12-12T23:09:00.252985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3020011100 111
 
11.8%
3020011200 73
 
7.8%
3020012000 65
 
6.9%
3020014600 62
 
6.6%
3020011700 57
 
6.1%
3020012200 56
 
6.0%
3020014100 42
 
4.5%
3020011900 34
 
3.6%
3020011400 33
 
3.5%
3020012100 30
 
3.2%
Other values (33) 374
39.9%
ValueCountFrequency (%)
3020010500 12
 
1.3%
3020011100 111
11.8%
3020011200 73
7.8%
3020011300 18
 
1.9%
3020011400 33
 
3.5%
3020011500 29
 
3.1%
3020011600 27
 
2.9%
3020011700 57
6.1%
3020011800 12
 
1.3%
3020011900 34
 
3.6%
ValueCountFrequency (%)
3023011800 14
 
1.5%
3020015300 5
 
0.5%
3020015200 2
 
0.2%
3020015100 2
 
0.2%
3020015000 2
 
0.2%
3020014900 4
 
0.4%
3020014800 8
 
0.9%
3020014700 17
 
1.8%
3020014600 62
6.6%
3020014500 14
 
1.5%

법정동이름
Categorical

HIGH CORRELATION 

Distinct43
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
봉명동
111 
구암동
73 
지족동
65 
관평동
62 
장대동
57 
Other values (38)
569 

Length

Max length4
Median length3
Mean length2.909285
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지족동
2nd row봉명동
3rd row신성동
4th row봉명동
5th row구암동

Common Values

ValueCountFrequency (%)
봉명동 111
 
11.8%
구암동 73
 
7.8%
지족동 65
 
6.9%
관평동 62
 
6.6%
장대동 57
 
6.1%
궁동 56
 
6.0%
전민동 42
 
4.5%
노은동 34
 
3.6%
원신흥동 33
 
3.5%
죽동 30
 
3.2%
Other values (33) 374
39.9%

Length

2023-12-12T23:09:00.422524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
봉명동 111
 
11.8%
구암동 73
 
7.8%
지족동 65
 
6.9%
관평동 62
 
6.6%
장대동 57
 
6.1%
궁동 56
 
6.0%
전민동 42
 
4.5%
노은동 34
 
3.6%
원신흥동 33
 
3.5%
죽동 30
 
3.2%
Other values (33) 374
39.9%

Interactions

2023-12-12T23:08:56.666649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:55.271756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:55.729904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:56.187648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:56.790038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:55.393381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:55.837934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:56.313851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:56.919886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:55.522853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:55.967342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:56.466270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:57.032672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:55.616020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:56.069296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:56.576462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:09:00.533310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사상자수관할소방서화재유형피해액발화요인장소유형행정동코드행정동이름법정동코드법정동이름
사상자수1.0000.0770.3480.7730.6450.1340.0000.1230.0000.705
관할소방서0.0771.0000.0740.0000.1780.3220.8650.9520.2461.000
화재유형0.3480.0741.0000.2961.0000.5710.1340.1850.2320.228
피해액0.7730.0000.2961.0000.4550.1750.0000.1280.0000.638
발화요인0.6450.1781.0000.4551.0000.7040.2930.2990.3850.431
장소유형0.1340.3220.5710.1750.7041.0000.3170.4320.3420.625
행정동코드0.0000.8650.1340.0000.2930.3171.0001.0000.1520.996
행정동이름0.1230.9520.1850.1280.2990.4321.0001.0000.4110.990
법정동코드0.0000.2460.2320.0000.3850.3420.1520.4111.0001.000
법정동이름0.7051.0000.2280.6380.4310.6250.9960.9901.0001.000
2023-12-12T23:09:00.705847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발화요인행정동이름화재유형장소유형법정동이름관할소방서
발화요인1.0000.0950.9840.2940.0910.082
행정동이름0.0951.0000.0540.1350.8870.894
화재유형0.9840.0541.0000.1930.0730.040
장소유형0.2940.1350.1931.0000.2440.183
법정동이름0.0910.8870.0730.2441.0000.979
관할소방서0.0820.8940.0400.1830.9791.000
2023-12-12T23:09:01.152837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사상자수피해액행정동코드법정동코드관할소방서화재유형발화요인장소유형행정동이름법정동이름
사상자수1.0000.126-0.026-0.0290.0490.1760.2980.0650.0590.365
피해액0.1261.0000.0780.0910.0000.1670.2240.0960.0700.350
행정동코드-0.0260.0781.0000.7390.7840.0610.1100.1530.9970.948
법정동코드-0.0290.0910.7391.0000.3000.1740.3010.2650.3180.978
관할소방서0.0490.0000.7840.3001.0000.0400.0820.1830.8940.979
화재유형0.1760.1670.0610.1740.0401.0000.9840.1930.0540.073
발화요인0.2980.2240.1100.3010.0820.9841.0000.2940.0950.091
장소유형0.0650.0960.1530.2650.1830.1930.2941.0000.1350.244
행정동이름0.0590.0700.9970.3180.8940.0540.0950.1351.0000.887
법정동이름0.3650.3500.9480.9780.9790.0730.0910.2440.8871.000

Missing values

2023-12-12T23:08:57.175588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:08:57.352008image/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

발생일시사상자수관할소방서화재유형피해액발화요인장소유형시도코드시도이름시군구코드시군구이름행정동코드행정동이름법정동코드법정동이름
02017-08-09 13:13:000노은 119안전센터전기적 요인740반단선주거3000000000대전광역시3020000000유성구3020054800노은3동3020012000지족동
12020-09-14 02:00:000구암 119안전센터미상2086미상주거3000000000대전광역시3020000000유성구3020053000온천1동3020011100봉명동
22019-03-19 02:45:000도룡 119안전센터미상1279미상주거3000000000대전광역시3020000000유성구3020055000신성동3020012500신성동
32017-06-05 03:25:000구암 119안전센터부주의<NA>담배꽁초주거3000000000대전광역시3020000000유성구3020053000온천1동3020011100봉명동
42017-02-28 13:00:000구암 119안전센터부주의<NA>쓰레기 소각임야3000000000대전광역시3020000000유성구3020053000온천1동3020011200구암동
52017-03-24 12:10:000도룡 119안전센터부주의2132담배꽁초주거3000000000대전광역시3020000000유성구3020055000신성동3020012700도룡동
62018-12-11 13:07:000전민 119안전센터부주의1389불씨.불꽃.화원방치주거3000000000대전광역시3020000000유성구3020060000관평동3020014400용산동
72016-07-26 23:15:000도룡 119안전센터기타497기타기타3000000000대전광역시3020000000유성구3020055000신성동3023011800장동
82016-02-03 20:46:000구암 119안전센터전기적 요인8859절연열화에 의한 단락기타서비스3000000000대전광역시3020000000유성구3020052700상대동3020011200구암동
92016-03-29 12:22:000구암 119안전센터부주의<NA>담배꽁초기타3000000000대전광역시3020000000유성구3020052700상대동3020011200구암동
발생일시사상자수관할소방서화재유형피해액발화요인장소유형시도코드시도이름시군구코드시군구이름행정동코드행정동이름법정동코드법정동이름
9272020-09-27 12:02:000도룡 119안전센터교통사고2200교통사고자동차.철도차량3000000000대전광역시3020000000유성구3020055000신성동3020013000화암동
9282016-04-11 17:47:000도룡 119안전센터부주의<NA>담배꽁초임야3000000000대전광역시3020000000유성구3020055000신성동3020013000화암동
9292018-02-28 20:32:000도룡 119안전센터전기적 요인1727절연열화에 의한 단락주거3000000000대전광역시3020000000유성구3020055000신성동3020013000화암동
9302018-05-13 13:12:000도룡 119안전센터전기적 요인767압착.손상에 의한 단락기타3000000000대전광역시3020000000유성구3020055000신성동3020013000화암동
9312018-06-22 11:33:000도룡 119안전센터전기적 요인67압착.손상에 의한 단락기타3000000000대전광역시3020000000유성구3020055000신성동3020013000화암동
9322018-07-30 06:38:000도룡 119안전센터전기적 요인3766과부하/과전류자동차.철도차량3000000000대전광역시3020000000유성구3020055000신성동3020013000화암동
9332018-10-22 21:30:000도룡 119안전센터기계적 요인17468과열. 과부하자동차.철도차량3000000000대전광역시3020000000유성구3020055000신성동3020013000화암동
9342019-01-11 09:51:000도룡 119안전센터부주의106쓰레기 소각기타서비스3000000000대전광역시3020000000유성구3020055000신성동3020013000화암동
9352019-08-13 17:10:001도룡 119안전센터기계적 요인3025오일.연료누설자동차.철도차량3000000000대전광역시3020000000유성구3020055000신성동3020013000화암동
9362019-11-01 00:10:000도룡 119안전센터방화67141방화교육시설3000000000대전광역시3020000000유성구3020055000신성동3020013000화암동