Overview

Dataset statistics

Number of variables7
Number of observations79
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory59.7 B

Variable types

Categorical2
DateTime2
Text1
Numeric2

Alerts

발생장소 has unique valuesUnique
재산피해액(천원) has unique valuesUnique
인명피해재난규모 has 26 (32.9%) zerosZeros

Reproduction

Analysis started2024-04-14 03:19:02.301310
Analysis finished2024-04-14 03:19:04.709463
Duration2.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

Distinct26
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Memory size764.0 B
화성시
7 
이천시
7 
고양시
7 
남양주시
7 
용인시
6 
Other values (21)
45 

Length

Max length4
Median length3
Mean length3.1392405
Min length3

Unique

Unique8 ?
Unique (%)10.1%

Sample

1st row가평군
2nd row고양시
3rd row고양시
4th row고양시
5th row고양시

Common Values

ValueCountFrequency (%)
화성시 7
 
8.9%
이천시 7
 
8.9%
고양시 7
 
8.9%
남양주시 7
 
8.9%
용인시 6
 
7.6%
안산시 5
 
6.3%
안성시 5
 
6.3%
김포시 4
 
5.1%
포천시 3
 
3.8%
평택시 3
 
3.8%
Other values (16) 25
31.6%

Length

2024-04-14T12:19:04.767277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
화성시 7
 
8.9%
고양시 7
 
8.9%
남양주시 7
 
8.9%
이천시 7
 
8.9%
용인시 6
 
7.6%
안산시 5
 
6.3%
안성시 5
 
6.3%
김포시 4
 
5.1%
포천시 3
 
3.8%
평택시 3
 
3.8%
Other values (16) 25
31.6%

발생일자
Date

Distinct77
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size764.0 B
Minimum2007-08-09 00:00:00
Maximum2023-05-23 00:00:00
2024-04-14T12:19:04.865318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:19:04.980922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

발생시각
Date

Distinct76
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size764.0 B
Minimum2024-04-14 00:42:00
Maximum2024-04-14 23:58:00
2024-04-14T12:19:05.085710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:19:05.198694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

발생장소
Text

UNIQUE 

Distinct79
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size764.0 B
2024-04-14T12:19:05.390650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length13
Mean length7.556962
Min length2

Characters and Unicode

Total characters597
Distinct characters219
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)100.0%

Sample

1st row시콜스키S92 헬기
2nd row텐폴드프라자(비어DO)
3rd row숲예찬
4th row대한송유관공사
5th row고양종합버스터미널
ValueCountFrequency (%)
외 8
 
6.8%
창고 2
 
1.7%
공사장 2
 
1.7%
시콜스키s92 1
 
0.9%
용인타워 1
 
0.9%
크리스f&c물류창고 1
 
0.9%
대봉그린아파트 1
 
0.9%
삼원 1
 
0.9%
신안아파트 1
 
0.9%
원진산업 1
 
0.9%
Other values (98) 98
83.8%
2024-04-14T12:19:05.713742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
38
 
6.4%
스 17
 
2.8%
주 16
 
2.7%
) 14
 
2.3%
( 14
 
2.3%
아 13
 
2.2%
장 12
 
2.0%
공 12
 
2.0%
대 12
 
2.0%
물 11
 
1.8%
Other values (209) 438
73.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 474
79.4%
Space Separator 38
 
6.4%
Uppercase Letter 35
 
5.9%
Close Punctuation 14
 
2.3%
Open Punctuation 14
 
2.3%
Lowercase Letter 12
 
2.0%
Decimal Number 8
 
1.3%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 17
 
3.6%
주 16
 
3.4%
아 13
 
2.7%
장 12
 
2.5%
공 12
 
2.5%
대 12
 
2.5%
물 11
 
2.3%
류 10
 
2.1%
이 9
 
1.9%
리 8
 
1.7%
Other values (174) 354
74.7%
Uppercase Letter
ValueCountFrequency (%)
S 6
17.1%
L 5
14.3%
F 4
11.4%
D 3
8.6%
C 3
8.6%
E 2
 
5.7%
O 2
 
5.7%
T 1
 
2.9%
N 1
 
2.9%
P 1
 
2.9%
Other values (7) 7
20.0%
Lowercase Letter
ValueCountFrequency (%)
o 3
25.0%
e 2
16.7%
s 1
 
8.3%
y 1
 
8.3%
d 1
 
8.3%
m 1
 
8.3%
t 1
 
8.3%
k 1
 
8.3%
a 1
 
8.3%
Decimal Number
ValueCountFrequency (%)
2 4
50.0%
3 1
 
12.5%
1 1
 
12.5%
9 1
 
12.5%
8 1
 
12.5%
Space Separator
ValueCountFrequency (%)
38
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 474
79.4%
Common 76
 
12.7%
Latin 47
 
7.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 17
 
3.6%
주 16
 
3.4%
아 13
 
2.7%
장 12
 
2.5%
공 12
 
2.5%
대 12
 
2.5%
물 11
 
2.3%
류 10
 
2.1%
이 9
 
1.9%
리 8
 
1.7%
Other values (174) 354
74.7%
Latin
ValueCountFrequency (%)
S 6
 
12.8%
L 5
 
10.6%
F 4
 
8.5%
D 3
 
6.4%
C 3
 
6.4%
o 3
 
6.4%
E 2
 
4.3%
O 2
 
4.3%
e 2
 
4.3%
s 1
 
2.1%
Other values (16) 16
34.0%
Common
ValueCountFrequency (%)
38
50.0%
) 14
 
18.4%
( 14
 
18.4%
2 4
 
5.3%
& 2
 
2.6%
3 1
 
1.3%
1 1
 
1.3%
9 1
 
1.3%
8 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 474
79.4%
ASCII 123
 
20.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
38
30.9%
) 14
 
11.4%
( 14
 
11.4%
S 6
 
4.9%
L 5
 
4.1%
F 4
 
3.3%
2 4
 
3.3%
D 3
 
2.4%
C 3
 
2.4%
o 3
 
2.4%
Other values (25) 29
23.6%
Hangul
ValueCountFrequency (%)
스 17
 
3.6%
주 16
 
3.4%
아 13
 
2.7%
장 12
 
2.5%
공 12
 
2.5%
대 12
 
2.5%
물 11
 
2.3%
류 10
 
2.1%
이 9
 
1.9%
리 8
 
1.7%
Other values (174) 354
74.7%

화재원인
Categorical

Distinct9
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size764.0 B
미상
27 
부주의
23 
전기적 요인
12 
기타
4 
가스누출(폭발)
3 
Other values (4)
10 

Length

Max length8
Median length6
Mean length3.4303797
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전기적 요인
2nd row미상
3rd row전기적 요인
4th row부주의
5th row가스누출(폭발)

Common Values

ValueCountFrequency (%)
미상 27
34.2%
부주의 23
29.1%
전기적 요인 12
15.2%
기타 4
 
5.1%
가스누출(폭발) 3
 
3.8%
방화 3
 
3.8%
화학적 요인 3
 
3.8%
기계적 요인 2
 
2.5%
방화의심 2
 
2.5%

Length

2024-04-14T12:19:05.829630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T12:19:05.928710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미상 27
28.1%
부주의 23
24.0%
요인 17
17.7%
전기적 12
12.5%
기타 4
 
4.2%
가스누출(폭발 3
 
3.1%
방화 3
 
3.1%
화학적 3
 
3.1%
기계적 2
 
2.1%
방화의심 2
 
2.1%

인명피해재난규모
Real number (ℝ)

ZEROS 

Distinct26
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.101266
Minimum0
Maximum130
Zeros26
Zeros (%)32.9%
Negative0
Negative (%)0.0%
Memory size843.0 B
2024-04-14T12:19:06.033664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q316
95-th percentile50.2
Maximum130
Range130
Interquartile range (IQR)16

Descriptive statistics

Standard deviation22.965188
Coefficient of variation (CV)1.7528984
Kurtosis14.373649
Mean13.101266
Median Absolute Deviation (MAD)5
Skewness3.4799921
Sum1035
Variance527.39987
MonotonicityNot monotonic
2024-04-14T12:19:06.124625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 26
32.9%
10 6
 
7.6%
1 6
 
7.6%
18 4
 
5.1%
2 4
 
5.1%
13 3
 
3.8%
19 3
 
3.8%
14 3
 
3.8%
16 2
 
2.5%
20 2
 
2.5%
Other values (16) 20
25.3%
ValueCountFrequency (%)
0 26
32.9%
1 6
 
7.6%
2 4
 
5.1%
3 2
 
2.5%
4 1
 
1.3%
5 1
 
1.3%
7 1
 
1.3%
8 1
 
1.3%
10 6
 
7.6%
11 2
 
2.5%
ValueCountFrequency (%)
130 1
1.3%
124 1
1.3%
59 1
1.3%
52 1
1.3%
50 2
2.5%
48 1
1.3%
39 1
1.3%
26 1
1.3%
25 1
1.3%
20 2
2.5%

재산피해액(천원)
Real number (ℝ)

UNIQUE 

Distinct79
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16453405
Minimum1645
Maximum4.7432326 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2024-04-14T12:19:06.232007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1645
5-th percentile2985.6
Q1190124.5
median6234786
Q39192841
95-th percentile40378378
Maximum4.7432326 × 108
Range4.7432162 × 108
Interquartile range (IQR)9002716.5

Descriptive statistics

Standard deviation54866591
Coefficient of variation (CV)3.3346649
Kurtosis64.130281
Mean16453405
Median Absolute Deviation (MAD)5981464
Skewness7.7027345
Sum1.299819 × 109
Variance3.0103428 × 1015
MonotonicityNot monotonic
2024-04-14T12:19:06.340784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37600000 1
 
1.3%
253322 1
 
1.3%
498888 1
 
1.3%
64059648 1
 
1.3%
4594449 1
 
1.3%
5912804 1
 
1.3%
4983 1
 
1.3%
188283 1
 
1.3%
5237612 1
 
1.3%
1645 1
 
1.3%
Other values (69) 69
87.3%
ValueCountFrequency (%)
1645 1
1.3%
1695 1
1.3%
2306 1
1.3%
2433 1
1.3%
3047 1
1.3%
3547 1
1.3%
4983 1
1.3%
15611 1
1.3%
19371 1
1.3%
22882 1
1.3%
ValueCountFrequency (%)
474323261 1
1.3%
98872648 1
1.3%
72119776 1
1.3%
64059648 1
1.3%
37747126 1
1.3%
37600000 1
1.3%
36815600 1
1.3%
36721879 1
1.3%
32627210 1
1.3%
31860910 1
1.3%

Interactions

2024-04-14T12:19:04.402564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:19:04.176931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:19:04.485335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:19:04.322909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-14T12:19:06.412662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명발생일자발생시각발생장소화재원인인명피해재난규모재산피해액(천원)
시군명1.0000.9200.9261.0000.2300.6070.000
발생일자0.9201.0000.9871.0000.7151.0000.000
발생시각0.9260.9871.0001.0000.9930.9921.000
발생장소1.0001.0001.0001.0001.0001.0001.000
화재원인0.2300.7150.9931.0001.0000.4260.000
인명피해재난규모0.6071.0000.9921.0000.4261.0000.000
재산피해액(천원)0.0000.0001.0001.0000.0000.0001.000
2024-04-14T12:19:06.506844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명화재원인
시군명1.0000.038
화재원인0.0381.000
2024-04-14T12:19:06.575673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인명피해재난규모재산피해액(천원)시군명화재원인
인명피해재난규모1.000-0.4680.0000.166
재산피해액(천원)-0.4681.0000.0000.000
시군명0.0000.0001.0000.038
화재원인0.1660.0000.0381.000

Missing values

2024-04-14T12:19:04.573518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-14T12:19:04.664780image/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

시군명발생일자발생시각발생장소화재원인인명피해재난규모재산피해액(천원)
0가평군2008-07-1917:03시콜스키S92 헬기전기적 요인037600000
1고양시2015-12-0903:21텐폴드프라자(비어DO)미상18253322
2고양시2019-08-1221:56숲예찬전기적 요인14291404
3고양시2018-10-0710:56대한송유관공사부주의07715334
4고양시2014-05-2609:02고양종합버스터미널가스누출(폭발)12414466139
5고양시2014-12-1322:31이레21상가미상39361724
6고양시2012-01-1308:42명진프라자전기적 요인253047
7고양시2014-11-3022:38동주오피스텔방화20145397
8과천시2022-12-2913:49제2경인고속도로 방음터널미상5236721879
9광주시2018-08-2808:58(주)대연미상011362554
시군명발생일자발생시각발생장소화재원인인명피해재난규모재산피해액(천원)
69포천시2022-02-1601:07비주텍스 창고 외미상09355983
70포천시2021-04-0501:15(주)LD 물류방화의심06548228
71포천시2021-08-2400:42대일특수포장 외전기적 요인05820689
72화성시2023-04-2411:15대성마리프미상05873563
73화성시2022-09-3014:22화일약품화학적 요인162203686
74화성시2023-05-0602:03에이치에스티전기적 요인06450928
75화성시2018-09-1913:08싸이노스 제2공장부주의012584015
76화성시2017-09-1602:10(주)조이테크미상46070382
77화성시2017-02-0410:55메타폴리스 3층 상가동부주의188323780
78화성시2019-08-1804:08엔피씨케미칼미상09029699