Overview

Dataset statistics

Number of variables9
Number of observations4612
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory342.4 KiB
Average record size in memory76.0 B

Variable types

Numeric2
DateTime1
Categorical5
Text1

Dataset

Description최근 10년(2013년~2022년)간 전국 축사에서 발생한 화재 현황으로 축사는 돈사, 우사, 계사로 구분됨, 제공 항목은 발화일시, 시도, 시군구, 인명피해, 재산피해, 발화요인임
Author소방청
URLhttps://www.data.go.kr/data/15125556/fileData.do

Alerts

사망 is highly imbalanced (99.2%)Imbalance
부상 is highly imbalanced (93.4%)Imbalance
연번 has unique valuesUnique
재산피해(천원) has 48 (1.0%) zerosZeros

Reproduction

Analysis started2023-12-16 15:40:13.197346
Analysis finished2023-12-16 15:40:19.363292
Duration6.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct4612
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2306.5
Minimum1
Maximum4612
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.7 KiB
2023-12-16T15:40:19.804488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile231.55
Q11153.75
median2306.5
Q33459.25
95-th percentile4381.45
Maximum4612
Range4611
Interquartile range (IQR)2305.5

Descriptive statistics

Standard deviation1331.5141
Coefficient of variation (CV)0.57728769
Kurtosis-1.2
Mean2306.5
Median Absolute Deviation (MAD)1153
Skewness0
Sum10637578
Variance1772929.7
MonotonicityStrictly increasing
2023-12-16T15:40:20.539487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2885 1
 
< 0.1%
3081 1
 
< 0.1%
3080 1
 
< 0.1%
3079 1
 
< 0.1%
3078 1
 
< 0.1%
3077 1
 
< 0.1%
3076 1
 
< 0.1%
3075 1
 
< 0.1%
3074 1
 
< 0.1%
Other values (4602) 4602
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
4612 1
< 0.1%
4611 1
< 0.1%
4610 1
< 0.1%
4609 1
< 0.1%
4608 1
< 0.1%
4607 1
< 0.1%
4606 1
< 0.1%
4605 1
< 0.1%
4604 1
< 0.1%
4603 1
< 0.1%

화재발생년월일
Date

Distinct4609
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size36.2 KiB
Minimum2013-01-02 10:08:00
Maximum2022-12-31 20:49:00
2023-12-16T15:40:21.551859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:40:22.848003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

시도
Categorical

Distinct16
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size36.2 KiB
경기도
903 
충청남도
732 
경상북도
678 
전라북도
527 
전라남도
491 
Other values (11)
1281 

Length

Max length7
Median length4
Mean length4.0906331
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기도
2nd row전라북도
3rd row경상북도
4th row경상북도
5th row경기도

Common Values

ValueCountFrequency (%)
경기도 903
19.6%
충청남도 732
15.9%
경상북도 678
14.7%
전라북도 527
11.4%
전라남도 491
10.6%
경상남도 467
10.1%
강원특별자치도 290
 
6.3%
충청북도 287
 
6.2%
제주특별자치도 65
 
1.4%
세종특별자치시 42
 
0.9%
Other values (6) 130
 
2.8%

Length

2023-12-16T15:40:24.057592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 903
19.6%
충청남도 732
15.9%
경상북도 678
14.7%
전라북도 527
11.4%
전라남도 491
10.6%
경상남도 467
10.1%
강원특별자치도 290
 
6.3%
충청북도 287
 
6.2%
제주특별자치도 65
 
1.4%
세종특별자치시 42
 
0.9%
Other values (6) 130
 
2.8%

시군구
Text

Distinct179
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size36.2 KiB
2023-12-16T15:40:25.789776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.1879879
Min length2

Characters and Unicode

Total characters14703
Distinct characters122
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

Unique16 ?
Unique (%)0.3%

Sample

1st row파주시
2nd row김제시
3rd row울진군
4th row봉화군
5th row양평군
ValueCountFrequency (%)
포천시 112
 
2.4%
안성시 107
 
2.3%
이천시 104
 
2.2%
홍성군 99
 
2.1%
정읍시 91
 
1.9%
논산시 90
 
1.9%
익산시 81
 
1.7%
공주시 77
 
1.6%
화성시 76
 
1.6%
경주시 72
 
1.5%
Other values (171) 3770
80.6%
2023-12-16T15:40:27.942781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 2508
 
17.1%
군 2085
 
14.2%
주 774
 
5.3%
천 740
 
5.0%
성 534
 
3.6%
산 480
 
3.3%
안 386
 
2.6%
구 372
 
2.5%
양 318
 
2.2%
창 200
 
1.4%
Other values (112) 6306
42.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14636
99.5%
Space Separator 67
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 2508
17.1%
군 2085
 
14.2%
주 774
 
5.3%
천 740
 
5.1%
성 534
 
3.6%
산 480
 
3.3%
안 386
 
2.6%
구 372
 
2.5%
양 318
 
2.2%
창 200
 
1.4%
Other values (111) 6239
42.6%
Space Separator
ValueCountFrequency (%)
67
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14636
99.5%
Common 67
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 2508
17.1%
군 2085
 
14.2%
주 774
 
5.3%
천 740
 
5.1%
성 534
 
3.6%
산 480
 
3.3%
안 386
 
2.6%
구 372
 
2.5%
양 318
 
2.2%
창 200
 
1.4%
Other values (111) 6239
42.6%
Common
ValueCountFrequency (%)
67
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14636
99.5%
ASCII 67
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 2508
17.1%
군 2085
 
14.2%
주 774
 
5.3%
천 740
 
5.1%
성 534
 
3.6%
산 480
 
3.3%
안 386
 
2.6%
구 372
 
2.5%
양 318
 
2.2%
창 200
 
1.4%
Other values (111) 6239
42.6%
ASCII
ValueCountFrequency (%)
67
100.0%

사망
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.2 KiB
0
4609 
1
 
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 4609
99.9%
1 3
 
0.1%

Length

2023-12-16T15:40:28.628000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:40:29.177723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4609
99.9%
1 3
 
0.1%

부상
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size36.2 KiB
0
4517 
1
 
88
2
 
5
3
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 4517
97.9%
1 88
 
1.9%
2 5
 
0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Length

2023-12-16T15:40:29.569467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:40:30.355041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4517
97.9%
1 88
 
1.9%
2 5
 
0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

재산피해(천원)
Real number (ℝ)

ZEROS 

Distinct3764
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54913.604
Minimum0
Maximum4751542
Zeros48
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size40.7 KiB
2023-12-16T15:40:31.058781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile94
Q1730
median4964.5
Q340197.75
95-th percentile267654.6
Maximum4751542
Range4751542
Interquartile range (IQR)39467.75

Descriptive statistics

Standard deviation162868.05
Coefficient of variation (CV)2.9658962
Kurtosis201.96392
Mean54913.604
Median Absolute Deviation (MAD)4785
Skewness10.466823
Sum2.5326154 × 108
Variance2.6526001 × 1010
MonotonicityNot monotonic
2023-12-16T15:40:32.117600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48
 
1.0%
55 16
 
0.3%
220 10
 
0.2%
550 9
 
0.2%
132 8
 
0.2%
122 7
 
0.2%
463 7
 
0.2%
200 7
 
0.2%
100 7
 
0.2%
154 6
 
0.1%
Other values (3754) 4487
97.3%
ValueCountFrequency (%)
0 48
1.0%
3 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
11 3
 
0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
ValueCountFrequency (%)
4751542 1
< 0.1%
2848172 1
< 0.1%
2380895 1
< 0.1%
2157349 1
< 0.1%
1717551 1
< 0.1%
1638964 1
< 0.1%
1604891 1
< 0.1%
1596199 1
< 0.1%
1486986 1
< 0.1%
1450820 1
< 0.1%

발화요인
Categorical

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size36.2 KiB
전기적 요인
2094 
부주의
1170 
미상
728 
기계적 요인
379 
화학적 요인
 
101
Other values (4)
 
140

Length

Max length7
Median length6
Mean length4.5581093
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전기적 요인 2094
45.4%
부주의 1170
25.4%
미상 728
 
15.8%
기계적 요인 379
 
8.2%
화학적 요인 101
 
2.2%
기타 64
 
1.4%
자연적인 요인 64
 
1.4%
방화의심 6
 
0.1%
방화 6
 
0.1%

Length

2023-12-16T15:40:32.836491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:40:33.570146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
요인 2638
36.4%
전기적 2094
28.9%
부주의 1170
16.1%
미상 728
 
10.0%
기계적 379
 
5.2%
화학적 101
 
1.4%
기타 64
 
0.9%
자연적인 64
 
0.9%
방화의심 6
 
0.1%
방화 6
 
0.1%

장소분류
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size36.2 KiB
우사
1924 
돈사
1682 
계사
1006 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row우사
2nd row돈사
3rd row우사
4th row우사
5th row돈사

Common Values

ValueCountFrequency (%)
우사 1924
41.7%
돈사 1682
36.5%
계사 1006
21.8%

Length

2023-12-16T15:40:34.308036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:40:35.052431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
우사 1924
41.7%
돈사 1682
36.5%
계사 1006
21.8%

Interactions

2023-12-16T15:40:16.914410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:40:15.783175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:40:17.353069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:40:16.405821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-16T15:40:35.486310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시도사망부상재산피해(천원)발화요인장소분류
연번1.0000.0730.0000.0000.0540.0790.043
시도0.0731.0000.1010.0000.0300.2290.352
사망0.0000.1011.0000.0000.0000.0000.013
부상0.0000.0000.0001.0000.0430.0330.035
재산피해(천원)0.0540.0300.0000.0431.0000.0980.127
발화요인0.0790.2290.0000.0330.0981.0000.462
장소분류0.0430.3520.0130.0350.1270.4621.000
2023-12-16T15:40:36.152315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사망부상발화요인시도장소분류
사망1.0000.0000.0000.0790.021
부상0.0001.0000.0190.0000.026
발화요인0.0000.0191.0000.0960.230
시도0.0790.0000.0961.0000.205
장소분류0.0210.0260.2300.2051.000
2023-12-16T15:40:36.679026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번재산피해(천원)시도사망부상발화요인장소분류
연번1.0000.0400.0290.0000.0000.0360.026
재산피해(천원)0.0401.0000.0140.0000.0270.0510.085
시도0.0290.0141.0000.0790.0000.0960.205
사망0.0000.0000.0791.0000.0000.0000.021
부상0.0000.0270.0000.0001.0000.0190.026
발화요인0.0360.0510.0960.0000.0191.0000.230
장소분류0.0260.0850.2050.0210.0260.2301.000

Missing values

2023-12-16T15:40:18.168840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T15:40:18.982437image/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

연번화재발생년월일시도시군구사망부상재산피해(천원)발화요인장소분류
012013-01-02 10:08:00경기도파주시004467미상우사
122013-01-03 08:04:00전라북도김제시0080전기적 요인돈사
232013-01-03 11:38:00경상북도울진군00404전기적 요인우사
342013-01-03 14:18:00경상북도봉화군002265전기적 요인우사
452013-01-03 15:02:00경기도양평군00268704전기적 요인돈사
562013-01-03 17:08:00경상북도영천시00110전기적 요인돈사
672013-01-04 01:36:00강원특별자치도춘천시009665기타돈사
782013-01-04 11:50:00전라북도고창군001865부주의우사
892013-01-04 14:09:00충청남도논산시00204기계적 요인우사
9102013-01-04 21:00:00전라북도고창군00451기계적 요인우사
연번화재발생년월일시도시군구사망부상재산피해(천원)발화요인장소분류
460246032022-12-28 22:45:00경상남도의령군002280전기적 요인우사
460346042022-12-29 02:52:00울산광역시울주군00763부주의우사
460446052022-12-29 04:49:00경기도화성시001239화학적 요인돈사
460546062022-12-30 00:03:00경상북도영천시0060937미상계사
460646072022-12-30 10:50:00충청남도논산시0019478미상계사
460746082022-12-30 10:51:00경상북도경산시0015625미상우사
460846092022-12-30 11:26:00경기도고양시덕양구00266전기적 요인계사
460946102022-12-31 07:02:00경상남도합천군00177전기적 요인돈사
461046112022-12-31 07:28:00충청북도음성군00132부주의우사
461146122022-12-31 20:49:00제주특별자치도제주시009907전기적 요인돈사