Overview

Dataset statistics

Number of variables12
Number of observations223
Missing cells0
Missing cells (%)0.0%
Duplicate rows17
Duplicate rows (%)7.6%
Total size in memory23.4 KiB
Average record size in memory107.6 B

Variable types

Text1
Numeric6
Categorical5

Dataset

Description전기화재통계
Author한국전기안전공사
URLhttps://www.data.go.kr/data/15069715/fileData.do

Alerts

Dataset has 17 (7.6%) duplicate rowsDuplicates
전기적 is highly overall correlated with 기계적 and 3 other fieldsHigh correlation
기계적 is highly overall correlated with 전기적 and 5 other fieldsHigh correlation
부주의 is highly overall correlated with 전기적 and 6 other fieldsHigh correlation
방화 is highly overall correlated with 자연적 and 2 other fieldsHigh correlation
기타 is highly overall correlated with 자연적 and 2 other fieldsHigh correlation
원인미상 is highly overall correlated with 부주의 and 1 other fieldsHigh correlation
자연적 is highly overall correlated with 전기적 and 6 other fieldsHigh correlation
가스누출 is highly overall correlated with 기계적 and 5 other fieldsHigh correlation
교통사고 is highly overall correlated with 전기적 and 5 other fieldsHigh correlation
방화의심 is highly overall correlated with 기계적 and 3 other fieldsHigh correlation
화학적 is highly imbalanced (53.7%)Imbalance
자연적 is highly imbalanced (77.2%)Imbalance
가스누출 is highly imbalanced (72.8%)Imbalance
교통사고 is highly imbalanced (56.4%)Imbalance
방화의심 is highly imbalanced (60.9%)Imbalance
전기적 has 35 (15.7%) zerosZeros
기계적 has 83 (37.2%) zerosZeros
방화 has 184 (82.5%) zerosZeros
기타 has 163 (73.1%) zerosZeros
원인미상 has 77 (34.5%) zerosZeros

Reproduction

Analysis started2023-12-13 00:28:28.547622
Analysis finished2023-12-13 00:28:31.612237
Duration3.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관할소방서
Text

Distinct186
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-13T09:28:31.805548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.4035874
Min length4

Characters and Unicode

Total characters1205
Distinct characters121
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique168 ?
Unique (%)75.3%

Sample

1st row서울지역본부
2nd row서울본부직할
3rd row마포소방서
4th row용산소방서
5th row서대문소방서
ValueCountFrequency (%)
서부소방서 8
 
3.6%
중부소방서 7
 
3.1%
남부소방서 5
 
2.2%
동부소방서 5
 
2.2%
북부소방서 4
 
1.8%
강서소방서 2
 
0.9%
항만소방서 2
 
0.9%
부산진소방서 2
 
0.9%
금정소방서 2
 
0.9%
해운대소방서 2
 
0.9%
Other values (176) 184
82.5%
2023-12-13T09:28:32.157537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
서 190
15.8%
소 165
 
13.7%
방 165
 
13.7%
부 83
 
6.9%
지 45
 
3.7%
사 34
 
2.8%
본 27
 
2.2%
남 25
 
2.1%
산 25
 
2.1%
천 20
 
1.7%
Other values (111) 426
35.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1205
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 190
15.8%
소 165
 
13.7%
방 165
 
13.7%
부 83
 
6.9%
지 45
 
3.7%
사 34
 
2.8%
본 27
 
2.2%
남 25
 
2.1%
산 25
 
2.1%
천 20
 
1.7%
Other values (111) 426
35.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1205
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 190
15.8%
소 165
 
13.7%
방 165
 
13.7%
부 83
 
6.9%
지 45
 
3.7%
사 34
 
2.8%
본 27
 
2.2%
남 25
 
2.1%
산 25
 
2.1%
천 20
 
1.7%
Other values (111) 426
35.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1205
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
서 190
15.8%
소 165
 
13.7%
방 165
 
13.7%
부 83
 
6.9%
지 45
 
3.7%
사 34
 
2.8%
본 27
 
2.2%
남 25
 
2.1%
산 25
 
2.1%
천 20
 
1.7%
Other values (111) 426
35.4%

전기적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0179372
Minimum0
Maximum68
Zeros35
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-13T09:28:32.256645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile17.7
Maximum68
Range68
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.5387111
Coefficient of variation (CV)1.7016377
Kurtosis27.612888
Mean5.0179372
Median Absolute Deviation (MAD)2
Skewness4.6124466
Sum1119
Variance72.909587
MonotonicityNot monotonic
2023-12-13T09:28:32.339784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 41
18.4%
2 36
16.1%
0 35
15.7%
3 24
10.8%
4 20
9.0%
5 14
 
6.3%
6 13
 
5.8%
9 6
 
2.7%
15 5
 
2.2%
7 5
 
2.2%
Other values (13) 24
10.8%
ValueCountFrequency (%)
0 35
15.7%
1 41
18.4%
2 36
16.1%
3 24
10.8%
4 20
9.0%
5 14
 
6.3%
6 13
 
5.8%
7 5
 
2.2%
8 1
 
0.4%
9 6
 
2.7%
ValueCountFrequency (%)
68 2
 
0.9%
37 1
 
0.4%
36 2
 
0.9%
29 1
 
0.4%
23 1
 
0.4%
22 2
 
0.9%
21 1
 
0.4%
18 2
 
0.9%
15 5
2.2%
14 4
1.8%

기계적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2331839
Minimum0
Maximum32
Zeros83
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-13T09:28:32.426313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile7.9
Maximum32
Range32
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.9237521
Coefficient of variation (CV)1.7570215
Kurtosis22.180368
Mean2.2331839
Median Absolute Deviation (MAD)1
Skewness4.1617482
Sum498
Variance15.395831
MonotonicityNot monotonic
2023-12-13T09:28:32.505989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 83
37.2%
1 44
19.7%
3 33
 
14.8%
2 29
 
13.0%
4 11
 
4.9%
7 6
 
2.7%
5 4
 
1.8%
8 3
 
1.3%
22 2
 
0.9%
11 2
 
0.9%
Other values (6) 6
 
2.7%
ValueCountFrequency (%)
0 83
37.2%
1 44
19.7%
2 29
 
13.0%
3 33
 
14.8%
4 11
 
4.9%
5 4
 
1.8%
6 1
 
0.4%
7 6
 
2.7%
8 3
 
1.3%
10 1
 
0.4%
ValueCountFrequency (%)
32 1
 
0.4%
22 2
 
0.9%
19 1
 
0.4%
18 1
 
0.4%
16 1
 
0.4%
11 2
 
0.9%
10 1
 
0.4%
8 3
1.3%
7 6
2.7%
6 1
 
0.4%

화학적
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
0
179 
1
33 
2
 
6
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 179
80.3%
1 33
 
14.8%
2 6
 
2.7%
3 5
 
2.2%

Length

2023-12-13T09:28:32.598023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:28:32.899457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 179
80.3%
1 33
 
14.8%
2 6
 
2.7%
3 5
 
2.2%

자연적
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
0
209 
1
 
13
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
0 209
93.7%
1 13
 
5.8%
2 1
 
0.4%

Length

2023-12-13T09:28:32.977958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:28:33.061910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 209
93.7%
1 13
 
5.8%
2 1
 
0.4%

가스누출
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
0
200 
1
 
13
2
 
7
3
 
2
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 200
89.7%
1 13
 
5.8%
2 7
 
3.1%
3 2
 
0.9%
6 1
 
0.4%

Length

2023-12-13T09:28:33.140753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:28:33.231383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 200
89.7%
1 13
 
5.8%
2 7
 
3.1%
3 2
 
0.9%
6 1
 
0.4%

교통사고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
0
178 
1
28 
2
 
10
3
 
6
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row3
2nd row2
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 178
79.8%
1 28
 
12.6%
2 10
 
4.5%
3 6
 
2.7%
6 1
 
0.4%

Length

2023-12-13T09:28:33.333516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:28:33.432357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 178
79.8%
1 28
 
12.6%
2 10
 
4.5%
3 6
 
2.7%
6 1
 
0.4%

부주의
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.273543
Minimum1
Maximum271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-13T09:28:33.535142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median9
Q315.5
95-th percentile56.7
Maximum271
Range270
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation29.995068
Coefficient of variation (CV)1.7364746
Kurtosis37.921271
Mean17.273543
Median Absolute Deviation (MAD)5
Skewness5.4585242
Sum3852
Variance899.70412
MonotonicityNot monotonic
2023-12-13T09:28:33.640119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 18
 
8.1%
1 17
 
7.6%
3 17
 
7.6%
9 14
 
6.3%
8 14
 
6.3%
7 12
 
5.4%
6 12
 
5.4%
12 10
 
4.5%
10 10
 
4.5%
13 10
 
4.5%
Other values (42) 89
39.9%
ValueCountFrequency (%)
1 17
7.6%
2 8
3.6%
3 17
7.6%
4 8
3.6%
5 18
8.1%
6 12
5.4%
7 12
5.4%
8 14
6.3%
9 14
6.3%
10 10
4.5%
ValueCountFrequency (%)
271 1
0.4%
242 1
0.4%
139 1
0.4%
117 1
0.4%
106 1
0.4%
100 1
0.4%
79 1
0.4%
61 2
0.9%
58 1
0.4%
57 2
0.9%

방화
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2690583
Minimum0
Maximum7
Zeros184
Zeros (%)82.5%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-13T09:28:33.720639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7822827
Coefficient of variation (CV)2.907484
Kurtosis30.756084
Mean0.2690583
Median Absolute Deviation (MAD)0
Skewness4.8421209
Sum60
Variance0.61196623
MonotonicityNot monotonic
2023-12-13T09:28:33.797326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 184
82.5%
1 30
 
13.5%
2 4
 
1.8%
4 3
 
1.3%
7 1
 
0.4%
3 1
 
0.4%
ValueCountFrequency (%)
0 184
82.5%
1 30
 
13.5%
2 4
 
1.8%
3 1
 
0.4%
4 3
 
1.3%
7 1
 
0.4%
ValueCountFrequency (%)
7 1
 
0.4%
4 3
 
1.3%
3 1
 
0.4%
2 4
 
1.8%
1 30
 
13.5%
0 184
82.5%

방화의심
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
0
182 
1
30 
2
 
6
3
 
4
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row3
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 182
81.6%
1 30
 
13.5%
2 6
 
2.7%
3 4
 
1.8%
6 1
 
0.4%

Length

2023-12-13T09:28:33.880396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:28:33.958271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 182
81.6%
1 30
 
13.5%
2 6
 
2.7%
3 4
 
1.8%
6 1
 
0.4%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64573991
Minimum0
Maximum11
Zeros163
Zeros (%)73.1%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-13T09:28:34.050368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6316319
Coefficient of variation (CV)2.5267633
Kurtosis18.153165
Mean0.64573991
Median Absolute Deviation (MAD)0
Skewness3.975264
Sum144
Variance2.6622228
MonotonicityNot monotonic
2023-12-13T09:28:34.135401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 163
73.1%
1 33
 
14.8%
2 10
 
4.5%
3 6
 
2.7%
4 4
 
1.8%
9 2
 
0.9%
6 1
 
0.4%
11 1
 
0.4%
10 1
 
0.4%
7 1
 
0.4%
ValueCountFrequency (%)
0 163
73.1%
1 33
 
14.8%
2 10
 
4.5%
3 6
 
2.7%
4 4
 
1.8%
5 1
 
0.4%
6 1
 
0.4%
7 1
 
0.4%
9 2
 
0.9%
10 1
 
0.4%
ValueCountFrequency (%)
11 1
 
0.4%
10 1
 
0.4%
9 2
 
0.9%
7 1
 
0.4%
6 1
 
0.4%
5 1
 
0.4%
4 4
 
1.8%
3 6
 
2.7%
2 10
 
4.5%
1 33
14.8%

원인미상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5022422
Minimum0
Maximum38
Zeros77
Zeros (%)34.5%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-13T09:28:34.239614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32.5
95-th percentile9.9
Maximum38
Range38
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation4.2499332
Coefficient of variation (CV)1.69845
Kurtosis24.39644
Mean2.5022422
Median Absolute Deviation (MAD)1
Skewness4.0455247
Sum558
Variance18.061932
MonotonicityNot monotonic
2023-12-13T09:28:34.332231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 77
34.5%
1 52
23.3%
2 38
17.0%
5 11
 
4.9%
4 9
 
4.0%
3 7
 
3.1%
8 6
 
2.7%
6 5
 
2.2%
7 4
 
1.8%
9 2
 
0.9%
Other values (10) 12
 
5.4%
ValueCountFrequency (%)
0 77
34.5%
1 52
23.3%
2 38
17.0%
3 7
 
3.1%
4 9
 
4.0%
5 11
 
4.9%
6 5
 
2.2%
7 4
 
1.8%
8 6
 
2.7%
9 2
 
0.9%
ValueCountFrequency (%)
38 1
0.4%
20 1
0.4%
19 1
0.4%
18 1
0.4%
17 1
0.4%
15 1
0.4%
13 1
0.4%
12 1
0.4%
11 2
0.9%
10 2
0.9%

Interactions

2023-12-13T09:28:31.018759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:29.015529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:29.414162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:29.817842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:30.247231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:30.636340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:31.084534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:29.083807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:29.481217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:29.881788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:30.314057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:30.704520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:31.157669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:29.149395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:29.546828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:29.955109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:30.376797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:30.764274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:31.219324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:29.211743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:29.607014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:30.030781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:30.441319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:30.823527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:31.288047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:29.281240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:29.685572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:30.113335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:30.506796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:30.892771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:31.348252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:29.343561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:29.749236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:30.182349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:30.568311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:28:30.953326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:28:34.403720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전기적기계적화학적자연적가스누출교통사고부주의방화방화의심기타원인미상
전기적1.0000.9280.5450.6800.6090.6820.8410.6260.6210.7110.909
기계적0.9281.0000.5570.7720.7230.6920.8560.7650.6580.7680.901
화학적0.5450.5571.0000.3360.4200.4270.7820.4780.4020.5450.525
자연적0.6800.7720.3361.0000.7150.7470.8190.9410.3450.8060.599
가스누출0.6090.7230.4200.7151.0000.8630.7780.6870.8020.7710.619
교통사고0.6820.6920.4270.7470.8631.0000.7780.6560.6400.6620.662
부주의0.8410.8560.7820.8190.7780.7781.0000.7460.7370.8650.872
방화0.6260.7650.4780.9410.6870.6560.7461.0000.5620.6820.644
방화의심0.6210.6580.4020.3450.8020.6400.7370.5621.0000.7490.728
기타0.7110.7680.5450.8060.7710.6620.8650.6820.7491.0000.728
원인미상0.9090.9010.5250.5990.6190.6620.8720.6440.7280.7281.000
2023-12-13T09:28:34.500416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가스누출화학적방화의심자연적교통사고
가스누출1.0000.3540.4250.7000.506
화학적0.3541.0000.3370.3240.360
방화의심0.4250.3371.0000.2770.286
자연적0.7000.3240.2771.0000.743
교통사고0.5060.3600.2860.7431.000
2023-12-13T09:28:34.578847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전기적기계적부주의방화기타원인미상화학적자연적가스누출교통사고방화의심
전기적1.0000.5400.7120.1560.3700.4200.4050.5840.4470.5250.460
기계적0.5401.0000.5740.2020.4040.4440.4730.7070.5730.5440.533
부주의0.7120.5741.0000.2260.4330.5320.4440.7440.6230.6220.569
방화0.1560.2020.2261.0000.1660.2210.3260.7010.5490.5150.423
기타0.3700.4040.4330.1661.0000.2670.3770.5070.5810.4560.553
원인미상0.4200.4440.5320.2210.2671.0000.4090.3910.4040.4450.515
화학적0.4050.4730.4440.3260.3770.4091.0000.3240.3540.3600.337
자연적0.5840.7070.7440.7010.5070.3910.3241.0000.7000.7430.277
가스누출0.4470.5730.6230.5490.5810.4040.3540.7001.0000.5060.425
교통사고0.5250.5440.6220.5150.4560.4450.3600.7430.5061.0000.286
방화의심0.4600.5330.5690.4230.5530.5150.3370.2770.4250.2861.000

Missing values

2023-12-13T09:28:31.443067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:28:31.565530image/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

관할소방서전기적기계적화학적자연적가스누출교통사고부주의방화방화의심기타원인미상
0서울지역본부6818101327113438
1서울본부직할13300026110212
2마포소방서200000120001
3용산소방서10000070005
4서대문소방서100000100002
5종로소방서33000091001
6중부소방서20000290011
7은평소방서400000140012
8서울동부지사1170000510016
9동대문소방서11000060011
관할소방서전기적기계적화학적자연적가스누출교통사고부주의방화방화의심기타원인미상
213함양소방서11000070000
214밀양창녕지사21000050011
215밀양소방서11000030001
216창녕소방서10000020010
217제주지역본부54000290108
218제주본부직할54000290108
219제주소방서21000140002
220서부소방서10000020003
221서귀포소방서10000020102
222동부소방서13000110001

Duplicate rows

Most frequently occurring

관할소방서전기적기계적화학적자연적가스누출교통사고부주의방화방화의심기타원인미상# duplicates
0금정소방서240000500012
1남부소방서530000800112
2부산진소방서100000500202
3삼척소방서110000100002
4서부소방서021000700022
5서부소방서210000900022
6속초소방서100000600002
7수성소방서3010011000022
8안성소방서6000201400002
9영월소방서210001100002