Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells1510
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory110.0 B

Variable types

DateTime1
Categorical4
Text2
Numeric5

Dataset

Description전국 화재발생 데이터로 시도별 화재발생일, 화재발생 주소, 화재원인, 인명피해, 재산피해 관련 2021년 자료입니다.
URLhttps://www.data.go.kr/data/15044003/fileData.do

Alerts

장소중분류 is highly overall correlated with 장소대분류High correlation
장소대분류 is highly overall correlated with 장소중분류High correlation
인명피해소계 is highly overall correlated with 부상High correlation
부상 is highly overall correlated with 인명피해소계High correlation
재산피해소계 is highly overall correlated with 부동산 and 1 other fieldsHigh correlation
부동산 is highly overall correlated with 재산피해소계High correlation
동산 is highly overall correlated with 재산피해소계High correlation
사망 is highly imbalanced (96.4%)Imbalance
부동산 has 755 (7.5%) missing valuesMissing
동산 has 755 (7.5%) missing valuesMissing
인명피해소계 is highly skewed (γ1 = 41.51354663)Skewed
부상 is highly skewed (γ1 = 46.04949879)Skewed
재산피해소계 is highly skewed (γ1 = 30.10711019)Skewed
부동산 is highly skewed (γ1 = 32.45743319)Skewed
동산 is highly skewed (γ1 = 42.66000424)Skewed
인명피해소계 has 9580 (95.8%) zerosZeros
부상 has 9650 (96.5%) zerosZeros
재산피해소계 has 766 (7.7%) zerosZeros
부동산 has 4028 (40.3%) zerosZeros
동산 has 603 (6.0%) zerosZeros

Reproduction

Analysis started2023-12-12 20:50:49.817104
Analysis finished2023-12-12 20:50:54.657801
Duration4.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

화재발생년월일
Date

Distinct9875
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-01-01 00:00:00
Maximum2021-12-31 23:25:00
2023-12-13T05:50:54.719787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:54.865331image/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
2217 
서울특별시
1389 
경상남도
790 
경상북도
775 
전라남도
649 
Other values (12)
4180 

Length

Max length7
Median length5
Mean length4.1299
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row광주광역시
2nd row전라북도
3rd row충청남도
4th row전라북도
5th row경상남도

Common Values

ValueCountFrequency (%)
경기도 2217
22.2%
서울특별시 1389
13.9%
경상남도 790
 
7.9%
경상북도 775
 
7.8%
전라남도 649
 
6.5%
부산광역시 636
 
6.4%
충청남도 561
 
5.6%
전라북도 555
 
5.5%
강원도 468
 
4.7%
충청북도 395
 
4.0%
Other values (7) 1565
15.7%

Length

2023-12-13T05:50:55.007804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 2217
22.2%
서울특별시 1389
13.9%
경상남도 790
 
7.9%
경상북도 775
 
7.8%
전라남도 649
 
6.5%
부산광역시 636
 
6.4%
충청남도 561
 
5.6%
전라북도 555
 
5.5%
강원도 468
 
4.7%
충청북도 395
 
4.0%
Other values (7) 1565
15.7%

시군구
Text

Distinct228
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:50:55.316057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.3671
Min length2

Characters and Unicode

Total characters33671
Distinct characters143
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

Unique0 ?
Unique (%)0.0%

Sample

1st row북구
2nd row진안군
3rd row예산군
4th row전주시덕진구
5th row진주시
ValueCountFrequency (%)
서구 242
 
2.4%
북구 191
 
1.9%
중구 168
 
1.6%
강서구 166
 
1.6%
남구 156
 
1.5%
화성시 154
 
1.5%
동구 150
 
1.5%
창원시 135
 
1.3%
청주시 122
 
1.2%
김해시 117
 
1.1%
Other values (220) 8656
84.4%
2023-12-13T05:50:55.759274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 4843
 
14.4%
구 4592
 
13.6%
군 2111
 
6.3%
주 1314
 
3.9%
산 1016
 
3.0%
성 871
 
2.6%
천 870
 
2.6%
서 851
 
2.5%
양 840
 
2.5%
남 779
 
2.3%
Other values (133) 15584
46.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 33414
99.2%
Space Separator 257
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 4843
 
14.5%
구 4592
 
13.7%
군 2111
 
6.3%
주 1314
 
3.9%
산 1016
 
3.0%
성 871
 
2.6%
천 870
 
2.6%
서 851
 
2.5%
양 840
 
2.5%
남 779
 
2.3%
Other values (132) 15327
45.9%
Space Separator
ValueCountFrequency (%)
257
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 33414
99.2%
Common 257
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 4843
 
14.5%
구 4592
 
13.7%
군 2111
 
6.3%
주 1314
 
3.9%
산 1016
 
3.0%
성 871
 
2.6%
천 870
 
2.6%
서 851
 
2.5%
양 840
 
2.5%
남 779
 
2.3%
Other values (132) 15327
45.9%
Common
ValueCountFrequency (%)
257
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 33414
99.2%
ASCII 257
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 4843
 
14.5%
구 4592
 
13.7%
군 2111
 
6.3%
주 1314
 
3.9%
산 1016
 
3.0%
성 871
 
2.6%
천 870
 
2.6%
서 851
 
2.5%
양 840
 
2.5%
남 779
 
2.3%
Other values (132) 15327
45.9%
ASCII
ValueCountFrequency (%)
257
100.0%

인명피해소계
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0568
Minimum0
Maximum33
Zeros9580
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:50:55.885690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum33
Range33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.45650257
Coefficient of variation (CV)8.0370171
Kurtosis2765.2736
Mean0.0568
Median Absolute Deviation (MAD)0
Skewness41.513547
Sum568
Variance0.2083946
MonotonicityNot monotonic
2023-12-13T05:50:55.982095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 9580
95.8%
1 353
 
3.5%
2 46
 
0.5%
3 10
 
0.1%
4 4
 
< 0.1%
9 2
 
< 0.1%
5 1
 
< 0.1%
8 1
 
< 0.1%
6 1
 
< 0.1%
33 1
 
< 0.1%
ValueCountFrequency (%)
0 9580
95.8%
1 353
 
3.5%
2 46
 
0.5%
3 10
 
0.1%
4 4
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
9 2
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
4 4
 
< 0.1%
3 10
 
0.1%
2 46
 
0.5%
1 353
3.5%

사망
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9917 
1
 
78
2
 
4
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 9917
99.2%
1 78
 
0.8%
2 4
 
< 0.1%
3 1
 
< 0.1%

Length

2023-12-13T05:50:56.106087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:50:56.196733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9917
99.2%
1 78
 
0.8%
2 4
 
< 0.1%
3 1
 
< 0.1%

부상
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0479
Minimum0
Maximum33
Zeros9650
Zeros (%)96.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:50:56.273762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum33
Range33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.43889048
Coefficient of variation (CV)9.1626405
Kurtosis3229.303
Mean0.0479
Median Absolute Deviation (MAD)0
Skewness46.049499
Sum479
Variance0.19262485
MonotonicityNot monotonic
2023-12-13T05:50:56.397408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 9650
96.5%
1 294
 
2.9%
2 39
 
0.4%
3 8
 
0.1%
8 2
 
< 0.1%
6 2
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
9 1
 
< 0.1%
33 1
 
< 0.1%
ValueCountFrequency (%)
0 9650
96.5%
1 294
 
2.9%
2 39
 
0.4%
3 8
 
0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
33 1
 
< 0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
9 1
 
< 0.1%
8 2
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%
4 2
 
< 0.1%
3 8
 
0.1%
2 39
 
0.4%
1 294
 
2.9%
0 9650
96.5%

재산피해소계
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct4132
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15655.565
Minimum0
Maximum8831129
Zeros766
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:50:56.603775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1102
median591.5
Q33550.5
95-th percentile34576.6
Maximum8831129
Range8831129
Interquartile range (IQR)3448.5

Descriptive statistics

Standard deviation159615.53
Coefficient of variation (CV)10.19545
Kurtosis1243.5601
Mean15655.565
Median Absolute Deviation (MAD)583.5
Skewness30.10711
Sum1.5655565 × 108
Variance2.5477117 × 1010
MonotonicityNot monotonic
2023-12-13T05:50:56.799239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 766
 
7.7%
11 107
 
1.1%
55 100
 
1.0%
110 91
 
0.9%
550 62
 
0.6%
220 59
 
0.6%
22 52
 
0.5%
1 52
 
0.5%
33 51
 
0.5%
330 48
 
0.5%
Other values (4122) 8612
86.1%
ValueCountFrequency (%)
0 766
7.7%
1 52
 
0.5%
2 30
 
0.3%
3 21
 
0.2%
4 13
 
0.1%
5 48
 
0.5%
6 11
 
0.1%
7 16
 
0.2%
8 20
 
0.2%
9 35
 
0.4%
ValueCountFrequency (%)
8831129 1
< 0.1%
4808902 1
< 0.1%
4680999 1
< 0.1%
4589061 1
< 0.1%
3621626 1
< 0.1%
3358766 1
< 0.1%
2940861 1
< 0.1%
2848379 1
< 0.1%
2432831 1
< 0.1%
2297223 1
< 0.1%

부동산
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct2936
Distinct (%)31.8%
Missing755
Missing (%)7.5%
Infinite0
Infinite (%)0.0%
Mean7232.5525
Minimum0
Maximum3911857
Zeros4028
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:50:56.979651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median82
Q31244
95-th percentile16059
Maximum3911857
Range3911857
Interquartile range (IQR)1244

Descriptive statistics

Standard deviation81192.132
Coefficient of variation (CV)11.225931
Kurtosis1296.3538
Mean7232.5525
Median Absolute Deviation (MAD)82
Skewness32.457433
Sum66864948
Variance6.5921623 × 109
MonotonicityNot monotonic
2023-12-13T05:50:57.175440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4028
40.3%
99 19
 
0.2%
68 18
 
0.2%
56 16
 
0.2%
50 16
 
0.2%
66 15
 
0.1%
22 15
 
0.1%
44 14
 
0.1%
52 14
 
0.1%
79 14
 
0.1%
Other values (2926) 5076
50.8%
(Missing) 755
 
7.5%
ValueCountFrequency (%)
0 4028
40.3%
1 5
 
0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 6
 
0.1%
5 5
 
0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
8 9
 
0.1%
9 5
 
0.1%
ValueCountFrequency (%)
3911857 1
< 0.1%
3455586 1
< 0.1%
3259483 1
< 0.1%
1957764 1
< 0.1%
1493108 1
< 0.1%
1426946 1
< 0.1%
1403035 1
< 0.1%
1256652 1
< 0.1%
1178913 1
< 0.1%
1057609 1
< 0.1%

동산
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct2867
Distinct (%)31.0%
Missing755
Missing (%)7.5%
Infinite0
Infinite (%)0.0%
Mean9701.5361
Minimum0
Maximum7773520
Zeros603
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:50:57.370265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q156
median342
Q31936
95-th percentile19998
Maximum7773520
Range7773520
Interquartile range (IQR)1880

Descriptive statistics

Standard deviation113332.21
Coefficient of variation (CV)11.681883
Kurtosis2542.6495
Mean9701.5361
Median Absolute Deviation (MAD)331
Skewness42.660004
Sum89690701
Variance1.284419 × 1010
MonotonicityNot monotonic
2023-12-13T05:50:57.525827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 603
 
6.0%
11 135
 
1.4%
110 130
 
1.3%
55 119
 
1.2%
550 86
 
0.9%
220 75
 
0.8%
33 74
 
0.7%
22 74
 
0.7%
330 70
 
0.7%
5 67
 
0.7%
Other values (2857) 7812
78.1%
(Missing) 755
 
7.5%
ValueCountFrequency (%)
0 603
6.0%
1 54
 
0.5%
2 47
 
0.5%
3 29
 
0.3%
4 28
 
0.3%
5 67
 
0.7%
6 21
 
0.2%
7 25
 
0.2%
8 28
 
0.3%
9 47
 
0.5%
ValueCountFrequency (%)
7773520 1
< 0.1%
3186026 1
< 0.1%
2851138 1
< 0.1%
2184883 1
< 0.1%
1761948 1
< 0.1%
1701930 1
< 0.1%
1637280 1
< 0.1%
1625728 1
< 0.1%
1557858 1
< 0.1%
1463220 1
< 0.1%

장소대분류
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
주거
2719 
기타
1829 
산업시설
1420 
자동차,철도차량
1244 
생활서비스
987 
Other values (9)
1801 

Length

Max length9
Median length8
Mean length3.9418
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
주거 2719
27.2%
기타 1829
18.3%
산업시설 1420
14.2%
자동차,철도차량 1244
12.4%
생활서비스 987
 
9.9%
판매,업무시설 686
 
6.9%
기타서비스 493
 
4.9%
임야 275
 
2.8%
의료,복지시설 94
 
0.9%
교육시설 89
 
0.9%
Other values (4) 164
 
1.6%

Length

2023-12-13T05:50:57.669677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
주거 2719
27.2%
기타 1829
18.3%
산업시설 1420
14.2%
자동차,철도차량 1244
12.4%
생활서비스 987
 
9.9%
판매,업무시설 686
 
6.9%
기타서비스 493
 
4.9%
임야 275
 
2.8%
의료,복지시설 94
 
0.9%
교육시설 89
 
0.9%
Other values (4) 164
 
1.6%

장소중분류
Categorical

HIGH CORRELATION 

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
야외
1760 
단독주택
1352 
공동주택
1253 
자동차
1105 
음식점
712 
Other values (38)
3818 

Length

Max length7
Median length6
Mean length3.4806
Min length2

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row일상서비스
2nd row음식점
3rd row들불
4th row단독주택
5th row공동주택

Common Values

ValueCountFrequency (%)
야외 1760
17.6%
단독주택 1352
13.5%
공동주택 1253
12.5%
자동차 1105
11.1%
음식점 712
7.1%
공장시설 620
 
6.2%
기타건축물 493
 
4.9%
창고시설 380
 
3.8%
일반업무 303
 
3.0%
동식물시설 254
 
2.5%
Other values (33) 1768
17.7%

Length

2023-12-13T05:50:57.822732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
야외 1760
17.6%
단독주택 1352
13.5%
공동주택 1253
12.5%
자동차 1105
11.1%
음식점 712
7.1%
공장시설 620
 
6.2%
기타건축물 493
 
4.9%
창고시설 380
 
3.8%
일반업무 303
 
3.0%
동식물시설 254
 
2.5%
Other values (33) 1768
17.7%

장소소분류
Text

Distinct249
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:50:58.250271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length22
Mean length4.872
Min length1

Characters and Unicode

Total characters48720
Distinct characters285
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

Unique43 ?
Unique (%)0.4%

Sample

1st row이미용실
2nd row한식
3rd row논밭두렁
4th row단독주택
5th row아파트
ValueCountFrequency (%)
기타 1280
 
9.8%
단독주택 940
 
7.2%
기타야외 818
 
6.3%
아파트 744
 
5.7%
쓰레기 689
 
5.3%
승용자동차 565
 
4.3%
건축물 476
 
3.7%
화물자동차 361
 
2.8%
창고 361
 
2.8%
다세대주택 334
 
2.6%
Other values (272) 6450
49.5%
2023-12-13T05:50:58.770496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 3420
 
7.0%
3018
 
6.2%
타 2134
 
4.4%
주 2075
 
4.3%
택 1938
 
4.0%
물 1207
 
2.5%
동 1159
 
2.4%
차 1061
 
2.2%
자 1056
 
2.2%
단 967
 
2.0%
Other values (275) 30685
63.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 44530
91.4%
Space Separator 3018
 
6.2%
Other Punctuation 618
 
1.3%
Close Punctuation 264
 
0.5%
Open Punctuation 264
 
0.5%
Uppercase Letter 26
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 3420
 
7.7%
타 2134
 
4.8%
주 2075
 
4.7%
택 1938
 
4.4%
물 1207
 
2.7%
동 1159
 
2.6%
차 1061
 
2.4%
자 1056
 
2.4%
단 967
 
2.2%
독 941
 
2.1%
Other values (266) 28572
64.2%
Uppercase Letter
ValueCountFrequency (%)
P 8
30.8%
C 8
30.8%
S 5
19.2%
A 5
19.2%
Other Punctuation
ValueCountFrequency (%)
, 613
99.2%
/ 5
 
0.8%
Space Separator
ValueCountFrequency (%)
3018
100.0%
Close Punctuation
ValueCountFrequency (%)
) 264
100.0%
Open Punctuation
ValueCountFrequency (%)
( 264
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 44530
91.4%
Common 4164
 
8.5%
Latin 26
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 3420
 
7.7%
타 2134
 
4.8%
주 2075
 
4.7%
택 1938
 
4.4%
물 1207
 
2.7%
동 1159
 
2.6%
차 1061
 
2.4%
자 1056
 
2.4%
단 967
 
2.2%
독 941
 
2.1%
Other values (266) 28572
64.2%
Common
ValueCountFrequency (%)
3018
72.5%
, 613
 
14.7%
) 264
 
6.3%
( 264
 
6.3%
/ 5
 
0.1%
Latin
ValueCountFrequency (%)
P 8
30.8%
C 8
30.8%
S 5
19.2%
A 5
19.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 44530
91.4%
ASCII 4190
 
8.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 3420
 
7.7%
타 2134
 
4.8%
주 2075
 
4.7%
택 1938
 
4.4%
물 1207
 
2.7%
동 1159
 
2.6%
차 1061
 
2.4%
자 1056
 
2.4%
단 967
 
2.2%
독 941
 
2.1%
Other values (266) 28572
64.2%
ASCII
ValueCountFrequency (%)
3018
72.0%
, 613
 
14.6%
) 264
 
6.3%
( 264
 
6.3%
P 8
 
0.2%
C 8
 
0.2%
/ 5
 
0.1%
S 5
 
0.1%
A 5
 
0.1%

Interactions

2023-12-13T05:50:53.443678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:51.256384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:51.775098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:52.361598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:52.880504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:53.549435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:51.357663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:51.903268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:52.449347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:52.981445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:53.722760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:51.480592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:52.014426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:52.546143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:53.103279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:53.816993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:51.574874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:52.120985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:52.668612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:53.223609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:53.912723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:51.675160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:52.248679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:52.770308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:53.333366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:50:58.885256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도인명피해소계사망부상재산피해소계부동산동산장소대분류장소중분류
시도1.0000.0020.0290.0000.0640.0000.0600.2660.384
인명피해소계0.0021.0000.3340.9930.0000.0000.0000.0330.081
사망0.0290.3341.0000.1090.0530.2590.0180.0710.038
부상0.0000.9930.1091.0000.0000.0000.0000.0000.051
재산피해소계0.0640.0000.0530.0001.0000.9230.8520.0700.297
부동산0.0000.0000.2590.0000.9231.0000.7390.0740.231
동산0.0600.0000.0180.0000.8520.7391.0000.0310.000
장소대분류0.2660.0330.0710.0000.0700.0740.0311.0001.000
장소중분류0.3840.0810.0380.0510.2970.2310.0001.0001.000
2023-12-13T05:50:59.004645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
장소중분류장소대분류시도사망
장소중분류1.0000.9990.1120.019
장소대분류0.9991.0000.0930.040
시도0.1120.0931.0000.016
사망0.0190.0400.0161.000
2023-12-13T05:50:59.097775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인명피해소계부상재산피해소계부동산동산시도사망장소대분류장소중분류
인명피해소계1.0000.9100.1380.1420.1080.0010.1360.0190.041
부상0.9101.0000.1130.1190.0820.0000.0430.0000.026
재산피해소계0.1380.1131.0000.6320.8450.0290.0360.0260.123
부동산0.1420.1190.6321.0000.2930.0000.1810.0270.094
동산0.1080.0820.8450.2931.0000.0280.0110.0150.000
시도0.0010.0000.0290.0000.0281.0000.0160.0930.112
사망0.1360.0430.0360.1810.0110.0161.0000.0400.019
장소대분류0.0190.0000.0260.0270.0150.0930.0401.0000.999
장소중분류0.0410.0260.1230.0940.0000.1120.0190.9991.000

Missing values

2023-12-13T05:50:54.333349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:50:54.497487image/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.
2023-12-13T05:50:54.608427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

화재발생년월일시도시군구인명피해소계사망부상재산피해소계부동산동산장소대분류장소중분류장소소분류
56872021-02-16 18:42광주광역시북구0001415770657092생활서비스일상서비스이미용실
193602021-07-01 17:07전라북도진안군00050050생활서비스음식점한식
82052021-03-09 14:55충청남도예산군0002750275임야들불논밭두렁
303132021-11-05 11:01전라북도전주시덕진구00011011주거단독주택단독주택
40532021-02-03 06:11경상남도진주시000386271115주거공동주택아파트
92882021-03-19 23:23충청남도공주시000148501485자동차,철도차량자동차승용자동차
283652021-10-16 16:59경상남도양산시00016514520주거공동주택아파트
181372021-06-16 15:29경상북도고령군000824719105주거단독주택단독주택
128692021-04-21 15:07경기도오산시000198001980자동차,철도차량자동차화물자동차
137552021-04-27 18:02경상남도진주시0009900990자동차,철도차량농업기계경운기
화재발생년월일시도시군구인명피해소계사망부상재산피해소계부동산동산장소대분류장소중분류장소소분류
175882021-06-09 17:15전라남도보성군00011011임야들불기타 들불
311112021-11-13 19:09전라북도임실군00034034판매,업무시설판매시설전통시장
110512021-04-05 22:20서울특별시서초구0000<NA><NA>기타야외쓰레기
245272021-08-26 11:49대구광역시수성구00088088생활서비스음식점한식
54402021-02-14 16:08전라남도함평군0001012819193기타서비스기타건축물기타 건축물
306642021-11-08 14:25충청남도천안시서북구000165001650자동차,철도차량자동차승용자동차
26932021-01-20 22:29경상북도영주시0001294660634판매,업무시설일반업무기타 일반업무시설
112442021-04-07 19:10강원도춘천시000962631331생활서비스음식점기타 음식점
29222021-01-22 23:49경기도양주시00020686141454기타서비스기타건축물기타 건축물
275662021-10-05 21:01서울특별시중구00034034기타도로기타도로