Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory664.1 KiB
Average record size in memory68.0 B

Variable types

Numeric3
DateTime1
Categorical2
Text1

Dataset

Description최근10년간 전국에서 발생한 전체 화재 데이터 현황으로 제공하고 있는 항목으로는 발화일시, 인명피해, 재산피해, 시도, 시군구가 있음
Author소방청
URLhttps://www.data.go.kr/data/15125555/fileData.do

Alerts

사망 is highly imbalanced (97.2%)Imbalance
부상 is highly skewed (γ1 = 31.35617156)Skewed
재산피해(천원) is highly skewed (γ1 = 21.57455038)Skewed
연번 has unique valuesUnique
부상 has 9685 (96.9%) zerosZeros
재산피해(천원) has 1153 (11.5%) zerosZeros

Reproduction

Analysis started2023-12-16 15:04:41.357253
Analysis finished2023-12-16 15:04:57.247507
Duration15.89 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%
Mean47631.214
Minimum2
Maximum95281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-16T15:04:57.815717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4910.8
Q124259
median47617.5
Q371088.25
95-th percentile90408.25
Maximum95281
Range95279
Interquartile range (IQR)46829.25

Descriptive statistics

Standard deviation27336.323
Coefficient of variation (CV)0.57391616
Kurtosis-1.1852336
Mean47631.214
Median Absolute Deviation (MAD)23415
Skewness0.0097345114
Sum4.7631214 × 108
Variance7.4727458 × 108
MonotonicityNot monotonic
2023-12-16T15:04:59.171967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77631 1
 
< 0.1%
52082 1
 
< 0.1%
10618 1
 
< 0.1%
4437 1
 
< 0.1%
27283 1
 
< 0.1%
5908 1
 
< 0.1%
28196 1
 
< 0.1%
31843 1
 
< 0.1%
62698 1
 
< 0.1%
19448 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
13 1
< 0.1%
32 1
< 0.1%
55 1
< 0.1%
56 1
< 0.1%
64 1
< 0.1%
78 1
< 0.1%
84 1
< 0.1%
96 1
< 0.1%
98 1
< 0.1%
ValueCountFrequency (%)
95281 1
< 0.1%
95280 1
< 0.1%
95278 1
< 0.1%
95269 1
< 0.1%
95264 1
< 0.1%
95261 1
< 0.1%
95243 1
< 0.1%
95242 1
< 0.1%
95233 1
< 0.1%
95230 1
< 0.1%

화재발생년월일
Date

Distinct9944
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2013-01-01 00:05:00
Maximum2015-03-23 16:21:00
2023-12-16T15:05:00.340239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:05:01.366830image/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
2253 
서울특별시
1260 
경상남도
859 
충청남도
736 
경상북도
675 
Other values (12)
4217 

Length

Max length7
Median length5
Mean length4.3375
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전라북도
2nd row전라북도
3rd row서울특별시
4th row서울특별시
5th row충청남도

Common Values

ValueCountFrequency (%)
경기도 2253
22.5%
서울특별시 1260
12.6%
경상남도 859
 
8.6%
충청남도 736
 
7.4%
경상북도 675
 
6.8%
전라남도 592
 
5.9%
강원특별자치도 536
 
5.4%
부산광역시 524
 
5.2%
인천광역시 422
 
4.2%
전라북도 421
 
4.2%
Other values (7) 1722
17.2%

Length

2023-12-16T15:05:02.526642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 2253
22.5%
서울특별시 1260
12.6%
경상남도 859
 
8.6%
충청남도 736
 
7.4%
경상북도 675
 
6.8%
전라남도 592
 
5.9%
강원특별자치도 536
 
5.4%
부산광역시 524
 
5.2%
인천광역시 422
 
4.2%
전라북도 421
 
4.2%
Other values (7) 1722
17.2%

시군구
Text

Distinct231
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-16T15:05:03.581316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.338
Min length2

Characters and Unicode

Total characters33380
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 row전주시완산구
2nd row부안군
3rd row영등포구
4th row금천구
5th row천안시동남구
ValueCountFrequency (%)
서구 244
 
2.4%
남구 220
 
2.1%
동구 220
 
2.1%
중구 205
 
2.0%
북구 202
 
2.0%
창원시 160
 
1.6%
김해시 152
 
1.5%
화성시 142
 
1.4%
평택시 123
 
1.2%
제주시 107
 
1.0%
Other values (223) 8462
82.7%
2023-12-16T15:05:05.521878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 4900
 
14.7%
구 4548
 
13.6%
군 2084
 
6.2%
주 1230
 
3.7%
산 1027
 
3.1%
천 985
 
3.0%
양 869
 
2.6%
성 854
 
2.6%
동 823
 
2.5%
남 820
 
2.5%
Other values (132) 15240
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 33143
99.3%
Space Separator 237
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 4900
 
14.8%
구 4548
 
13.7%
군 2084
 
6.3%
주 1230
 
3.7%
산 1027
 
3.1%
천 985
 
3.0%
양 869
 
2.6%
성 854
 
2.6%
동 823
 
2.5%
남 820
 
2.5%
Other values (131) 15003
45.3%
Space Separator
ValueCountFrequency (%)
237
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 33143
99.3%
Common 237
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 4900
 
14.8%
구 4548
 
13.7%
군 2084
 
6.3%
주 1230
 
3.7%
산 1027
 
3.1%
천 985
 
3.0%
양 869
 
2.6%
성 854
 
2.6%
동 823
 
2.5%
남 820
 
2.5%
Other values (131) 15003
45.3%
Common
ValueCountFrequency (%)
237
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 33143
99.3%
ASCII 237
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 4900
 
14.8%
구 4548
 
13.7%
군 2084
 
6.3%
주 1230
 
3.7%
산 1027
 
3.1%
천 985
 
3.0%
양 869
 
2.6%
성 854
 
2.6%
동 823
 
2.5%
남 820
 
2.5%
Other values (131) 15003
45.3%
ASCII
ValueCountFrequency (%)
237
100.0%

사망
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9930 
1
 
61
2
 
7
4
 
1
3
 
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 9930
99.3%
1 61
 
0.6%
2 7
 
0.1%
4 1
 
< 0.1%
3 1
 
< 0.1%

Length

2023-12-16T15:05:06.245136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:05:06.788661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9930
99.3%
1 61
 
0.6%
2 7
 
0.1%
4 1
 
< 0.1%
3 1
 
< 0.1%

부상
Real number (ℝ)

SKEWED  ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0472
Minimum0
Maximum26
Zeros9685
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-16T15:05:07.229210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.42165144
Coefficient of variation (CV)8.9332933
Kurtosis1607.3364
Mean0.0472
Median Absolute Deviation (MAD)0
Skewness31.356172
Sum472
Variance0.17778994
MonotonicityNot monotonic
2023-12-16T15:05:07.766083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 9685
96.9%
1 253
 
2.5%
2 36
 
0.4%
3 10
 
0.1%
5 5
 
0.1%
7 4
 
< 0.1%
4 3
 
< 0.1%
6 2
 
< 0.1%
26 1
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
0 9685
96.9%
1 253
 
2.5%
2 36
 
0.4%
3 10
 
0.1%
4 3
 
< 0.1%
5 5
 
0.1%
6 2
 
< 0.1%
7 4
 
< 0.1%
14 1
 
< 0.1%
26 1
 
< 0.1%
ValueCountFrequency (%)
26 1
 
< 0.1%
14 1
 
< 0.1%
7 4
 
< 0.1%
6 2
 
< 0.1%
5 5
 
0.1%
4 3
 
< 0.1%
3 10
 
0.1%
2 36
 
0.4%
1 253
 
2.5%
0 9685
96.9%

재산피해(천원)
Real number (ℝ)

SKEWED  ZEROS 

Distinct3746
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7646.9734
Minimum0
Maximum1972568
Zeros1153
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-16T15:05:08.335575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q168
median441
Q32597.25
95-th percentile24397.25
Maximum1972568
Range1972568
Interquartile range (IQR)2529.25

Descriptive statistics

Standard deviation52309.123
Coefficient of variation (CV)6.8405002
Kurtosis613.06514
Mean7646.9734
Median Absolute Deviation (MAD)441
Skewness21.57455
Sum76469734
Variance2.7362444 × 109
MonotonicityNot monotonic
2023-12-16T15:05:09.407203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1153
 
11.5%
110 137
 
1.4%
55 121
 
1.2%
220 90
 
0.9%
330 87
 
0.9%
550 79
 
0.8%
50 78
 
0.8%
22 73
 
0.7%
11 69
 
0.7%
30 61
 
0.6%
Other values (3736) 8052
80.5%
ValueCountFrequency (%)
0 1153
11.5%
1 34
 
0.3%
2 10
 
0.1%
3 10
 
0.1%
4 6
 
0.1%
5 22
 
0.2%
6 8
 
0.1%
7 6
 
0.1%
8 18
 
0.2%
9 7
 
0.1%
ValueCountFrequency (%)
1972568 1
< 0.1%
1896891 1
< 0.1%
1558645 1
< 0.1%
1476625 1
< 0.1%
1187266 1
< 0.1%
1093825 1
< 0.1%
945028 1
< 0.1%
930281 1
< 0.1%
902183 1
< 0.1%
848903 1
< 0.1%

Interactions

2023-12-16T15:04:53.967443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:04:50.276729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:04:52.212240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:04:54.471730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:04:50.913626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:04:52.607428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:04:54.954080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:04:51.570698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:04:53.140491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-16T15:05:09.822814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시도사망부상재산피해(천원)
연번1.0000.0960.0000.0320.025
시도0.0961.0000.0190.0710.000
사망0.0000.0191.0000.3410.553
부상0.0320.0710.3411.0000.700
재산피해(천원)0.0250.0000.5530.7001.000
2023-12-16T15:05:10.294399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사망시도
사망1.0000.010
시도0.0101.000
2023-12-16T15:05:10.586588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번부상재산피해(천원)시도사망
연번1.000-0.006-0.0140.0370.000
부상-0.0061.0000.1260.0360.133
재산피해(천원)-0.0140.1261.0000.0000.360
시도0.0370.0360.0001.0000.010
사망0.0000.1330.3600.0101.000

Missing values

2023-12-16T15:04:55.871144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T15:04:56.881827image/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)
77630776312014-11-16 07:44:00전라북도전주시완산구003465
35780357812013-11-16 00:10:00전라북도부안군002244
54106541072014-04-04 03:50:00서울특별시영등포구006
74175741762014-10-13 21:39:00서울특별시금천구0037
63309633102014-06-16 19:31:00충청남도천안시동남구001650
24441244422013-07-21 02:30:00충청남도천안시서북구0012943
78443784442014-11-23 00:38:00경기도시흥시00198
93705937062015-03-16 11:09:00경기도파주시00198
844384442013-03-05 09:24:00인천광역시부평구001188
44504445052014-01-26 18:46:00부산광역시사상구00990
연번화재발생년월일시도시군구사망부상재산피해(천원)
79280792812014-12-02 09:01:00경기도양주시002530
82256822572014-12-25 13:34:00서울특별시광진구00310
784378442013-03-01 07:16:00서울특별시강북구00200
54726547272014-04-08 09:41:00경기도광명시00224
74147741482014-10-13 16:04:00경상북도영양군007147
48499485002014-02-25 15:53:00전라북도남원시00250
56479564802014-04-21 14:15:00서울특별시금천구00150
90587905882015-03-01 13:50:00경기도과천시001301
51065510662014-03-11 22:53:00전라남도여수시00746
91098910992015-03-05 07:03:00울산광역시울주군004400