Overview

Dataset statistics

Number of variables10
Number of observations117
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.3 KiB
Average record size in memory90.1 B

Variable types

Numeric9
Categorical1

Dataset

Description원인별(내과질환, 교통사고, 안전사고, 화재진화, 철도사고, 기타) 급여심의 가결 추이에 대한 데이터입니다. 1984년부터 시작됩니다.
URLhttps://www.data.go.kr/data/15054082/fileData.do

Alerts

연도 is highly overall correlated with 철도사고High correlation
합계 is highly overall correlated with 내과질환 and 6 other fieldsHigh correlation
내과질환 is highly overall correlated with 합계 and 3 other fieldsHigh correlation
교통사고(일반) is highly overall correlated with 합계 and 5 other fieldsHigh correlation
교통사고(오토바이) is highly overall correlated with 합계 and 5 other fieldsHigh correlation
안전사고 is highly overall correlated with 합계 and 5 other fieldsHigh correlation
화재진화 is highly overall correlated with 합계 and 5 other fieldsHigh correlation
철도사고 is highly overall correlated with 연도High correlation
기타 is highly overall correlated with 합계 and 4 other fieldsHigh correlation
구분 is highly overall correlated with 합계 and 1 other fieldsHigh correlation
교통사고(오토바이) has 13 (11.1%) zerosZeros
화재진화 has 35 (29.9%) zerosZeros
철도사고 has 43 (36.8%) zerosZeros

Reproduction

Analysis started2023-12-12 03:44:07.609644
Analysis finished2023-12-12 03:44:19.101073
Duration11.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2003
Minimum1984
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T12:44:19.472714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1984
5-th percentile1985.8
Q11993
median2003
Q32013
95-th percentile2020.2
Maximum2022
Range38
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.303036
Coefficient of variation (CV)0.0056430534
Kurtosis-1.2014655
Mean2003
Median Absolute Deviation (MAD)10
Skewness0
Sum234351
Variance127.75862
MonotonicityIncreasing
2023-12-12T12:44:19.623393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1984 3
 
2.6%
1985 3
 
2.6%
2006 3
 
2.6%
2007 3
 
2.6%
2008 3
 
2.6%
2009 3
 
2.6%
2010 3
 
2.6%
2011 3
 
2.6%
2012 3
 
2.6%
2013 3
 
2.6%
Other values (29) 87
74.4%
ValueCountFrequency (%)
1984 3
2.6%
1985 3
2.6%
1986 3
2.6%
1987 3
2.6%
1988 3
2.6%
1989 3
2.6%
1990 3
2.6%
1991 3
2.6%
1992 3
2.6%
1993 3
2.6%
ValueCountFrequency (%)
2022 3
2.6%
2021 3
2.6%
2020 3
2.6%
2019 3
2.6%
2018 3
2.6%
2017 3
2.6%
2016 3
2.6%
2015 3
2.6%
2014 3
2.6%
2013 3
2.6%

구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
공무상요양
39 
장해급여
39 
유족보상
33 
순직유족보상
6 

Length

Max length6
Median length4
Mean length4.4358974
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row유족보상
2nd row공무상요양
3rd row장해급여
4th row유족보상
5th row공무상요양

Common Values

ValueCountFrequency (%)
공무상요양 39
33.3%
장해급여 39
33.3%
유족보상 33
28.2%
순직유족보상 6
 
5.1%

Length

2023-12-12T12:44:19.780099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:44:19.903249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공무상요양 39
33.3%
장해급여 39
33.3%
유족보상 33
28.2%
순직유족보상 6
 
5.1%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1406.7094
Minimum42
Maximum6213
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T12:44:20.021758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile66
Q1133
median253
Q32841
95-th percentile5310.8
Maximum6213
Range6171
Interquartile range (IQR)2708

Descriptive statistics

Standard deviation1895.7092
Coefficient of variation (CV)1.3476196
Kurtosis-0.1316702
Mean1406.7094
Median Absolute Deviation (MAD)175
Skewness1.1902917
Sum164585
Variance3593713.2
MonotonicityNot monotonic
2023-12-12T12:44:20.208685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226 3
 
2.6%
249 3
 
2.6%
130 2
 
1.7%
65 2
 
1.7%
123 2
 
1.7%
93 2
 
1.7%
229 2
 
1.7%
66 2
 
1.7%
73 2
 
1.7%
4264 1
 
0.9%
Other values (96) 96
82.1%
ValueCountFrequency (%)
42 1
0.9%
47 1
0.9%
57 1
0.9%
65 2
1.7%
66 2
1.7%
69 1
0.9%
70 1
0.9%
73 2
1.7%
74 1
0.9%
76 1
0.9%
ValueCountFrequency (%)
6213 1
0.9%
6055 1
0.9%
5855 1
0.9%
5648 1
0.9%
5377 1
0.9%
5366 1
0.9%
5297 1
0.9%
5226 1
0.9%
5089 1
0.9%
4874 1
0.9%

내과질환
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.94872
Minimum9
Maximum722
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T12:44:20.403001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile18.6
Q142
median112
Q3193
95-th percentile370.2
Maximum722
Range713
Interquartile range (IQR)151

Descriptive statistics

Standard deviation122.62768
Coefficient of variation (CV)0.87001632
Kurtosis3.8816206
Mean140.94872
Median Absolute Deviation (MAD)72
Skewness1.6386776
Sum16491
Variance15037.549
MonotonicityNot monotonic
2023-12-12T12:44:20.576473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 4
 
3.4%
41 3
 
2.6%
26 3
 
2.6%
115 2
 
1.7%
201 2
 
1.7%
190 2
 
1.7%
108 2
 
1.7%
38 2
 
1.7%
42 2
 
1.7%
46 2
 
1.7%
Other values (85) 93
79.5%
ValueCountFrequency (%)
9 1
0.9%
10 1
0.9%
11 1
0.9%
12 1
0.9%
13 1
0.9%
17 1
0.9%
19 1
0.9%
20 1
0.9%
21 1
0.9%
25 1
0.9%
ValueCountFrequency (%)
722 1
0.9%
506 1
0.9%
446 1
0.9%
422 1
0.9%
391 1
0.9%
383 1
0.9%
367 1
0.9%
364 1
0.9%
334 1
0.9%
329 1
0.9%

교통사고(일반)
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225.21368
Minimum5
Maximum1207
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T12:44:20.744133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8.8
Q117
median49
Q3453
95-th percentile840
Maximum1207
Range1202
Interquartile range (IQR)436

Descriptive statistics

Standard deviation307.04807
Coefficient of variation (CV)1.3633633
Kurtosis0.59302215
Mean225.21368
Median Absolute Deviation (MAD)40
Skewness1.3470591
Sum26350
Variance94278.514
MonotonicityNot monotonic
2023-12-12T12:44:20.931866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 5
 
4.3%
16 4
 
3.4%
6 4
 
3.4%
17 4
 
3.4%
10 3
 
2.6%
27 2
 
1.7%
11 2
 
1.7%
26 2
 
1.7%
21 2
 
1.7%
14 2
 
1.7%
Other values (80) 87
74.4%
ValueCountFrequency (%)
5 1
 
0.9%
6 4
3.4%
8 1
 
0.9%
9 5
4.3%
10 3
2.6%
11 2
 
1.7%
12 1
 
0.9%
13 2
 
1.7%
14 2
 
1.7%
15 1
 
0.9%
ValueCountFrequency (%)
1207 1
0.9%
1159 1
0.9%
960 1
0.9%
868 1
0.9%
853 1
0.9%
844 1
0.9%
839 1
0.9%
788 1
0.9%
765 1
0.9%
753 1
0.9%

교통사고(오토바이)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.230769
Minimum0
Maximum406
Zeros13
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T12:44:21.114040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median13
Q373
95-th percentile283.4
Maximum406
Range406
Interquartile range (IQR)69

Descriptive statistics

Standard deviation98.384659
Coefficient of variation (CV)1.5317372
Kurtosis1.567208
Mean64.230769
Median Absolute Deviation (MAD)12
Skewness1.6556167
Sum7515
Variance9679.5411
MonotonicityNot monotonic
2023-12-12T12:44:21.286726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
11.1%
2 7
 
6.0%
10 6
 
5.1%
5 6
 
5.1%
4 5
 
4.3%
6 4
 
3.4%
1 4
 
3.4%
13 4
 
3.4%
15 3
 
2.6%
16 3
 
2.6%
Other values (50) 62
53.0%
ValueCountFrequency (%)
0 13
11.1%
1 4
 
3.4%
2 7
6.0%
3 2
 
1.7%
4 5
 
4.3%
5 6
5.1%
6 4
 
3.4%
7 3
 
2.6%
8 3
 
2.6%
9 1
 
0.9%
ValueCountFrequency (%)
406 1
0.9%
331 1
0.9%
328 1
0.9%
305 1
0.9%
289 1
0.9%
285 1
0.9%
283 2
1.7%
280 1
0.9%
263 1
0.9%
261 1
0.9%

안전사고
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)74.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean642.5812
Minimum1
Maximum3502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T12:44:21.485920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.8
Q116
median52
Q31162
95-th percentile2515.8
Maximum3502
Range3501
Interquartile range (IQR)1146

Descriptive statistics

Standard deviation979.97198
Coefficient of variation (CV)1.5250555
Kurtosis0.65119791
Mean642.5812
Median Absolute Deviation (MAD)45
Skewness1.3784721
Sum75182
Variance960345.07
MonotonicityNot monotonic
2023-12-12T12:44:21.704297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 5
 
4.3%
12 4
 
3.4%
21 3
 
2.6%
30 3
 
2.6%
58 3
 
2.6%
4 3
 
2.6%
7 3
 
2.6%
16 3
 
2.6%
18 3
 
2.6%
8 2
 
1.7%
Other values (77) 85
72.6%
ValueCountFrequency (%)
1 1
 
0.9%
2 1
 
0.9%
3 1
 
0.9%
4 3
2.6%
5 2
 
1.7%
6 5
4.3%
7 3
2.6%
8 2
 
1.7%
9 2
 
1.7%
11 2
 
1.7%
ValueCountFrequency (%)
3502 1
0.9%
3369 1
0.9%
3359 1
0.9%
3293 1
0.9%
2986 1
0.9%
2743 1
0.9%
2459 1
0.9%
2413 1
0.9%
2329 1
0.9%
2323 1
0.9%

화재진화
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.641026
Minimum0
Maximum105
Zeros35
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T12:44:21.915059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q333
95-th percentile75.4
Maximum105
Range105
Interquartile range (IQR)33

Descriptive statistics

Standard deviation26.449191
Coefficient of variation (CV)1.5893967
Kurtosis1.6640193
Mean16.641026
Median Absolute Deviation (MAD)2
Skewness1.6235008
Sum1947
Variance699.55968
MonotonicityNot monotonic
2023-12-12T12:44:22.128475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 35
29.9%
1 16
13.7%
2 14
 
12.0%
3 10
 
8.5%
16 3
 
2.6%
77 2
 
1.7%
43 2
 
1.7%
12 2
 
1.7%
39 2
 
1.7%
51 2
 
1.7%
Other values (24) 29
24.8%
ValueCountFrequency (%)
0 35
29.9%
1 16
13.7%
2 14
 
12.0%
3 10
 
8.5%
4 1
 
0.9%
5 2
 
1.7%
9 2
 
1.7%
12 2
 
1.7%
16 3
 
2.6%
21 1
 
0.9%
ValueCountFrequency (%)
105 1
0.9%
100 1
0.9%
97 1
0.9%
85 1
0.9%
77 2
1.7%
75 1
0.9%
67 1
0.9%
66 1
0.9%
65 1
0.9%
62 1
0.9%

철도사고
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3760684
Minimum0
Maximum171
Zeros43
Zeros (%)36.8%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T12:44:22.290364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q312
95-th percentile37
Maximum171
Range171
Interquartile range (IQR)12

Descriptive statistics

Standard deviation19.865811
Coefficient of variation (CV)2.1187784
Kurtosis39.322973
Mean9.3760684
Median Absolute Deviation (MAD)2
Skewness5.4723054
Sum1097
Variance394.65046
MonotonicityNot monotonic
2023-12-12T12:44:22.444940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 43
36.8%
1 10
 
8.5%
2 9
 
7.7%
9 5
 
4.3%
6 4
 
3.4%
4 4
 
3.4%
14 4
 
3.4%
7 4
 
3.4%
13 3
 
2.6%
10 3
 
2.6%
Other values (22) 28
23.9%
ValueCountFrequency (%)
0 43
36.8%
1 10
 
8.5%
2 9
 
7.7%
3 2
 
1.7%
4 4
 
3.4%
5 1
 
0.9%
6 4
 
3.4%
7 4
 
3.4%
9 5
 
4.3%
10 3
 
2.6%
ValueCountFrequency (%)
171 1
0.9%
87 1
0.9%
50 1
0.9%
49 1
0.9%
42 1
0.9%
41 1
0.9%
36 1
0.9%
29 1
0.9%
27 1
0.9%
26 2
1.7%

기타
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307.71795
Minimum0
Maximum2130
Zeros1
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T12:44:22.673519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18
median23
Q3326
95-th percentile1559
Maximum2130
Range2130
Interquartile range (IQR)318

Descriptive statistics

Standard deviation554.57145
Coefficient of variation (CV)1.802207
Kurtosis2.4290311
Mean307.71795
Median Absolute Deviation (MAD)17
Skewness1.9122111
Sum36003
Variance307549.5
MonotonicityNot monotonic
2023-12-12T12:44:22.882612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 9
 
7.7%
14 5
 
4.3%
8 4
 
3.4%
16 4
 
3.4%
22 4
 
3.4%
3 3
 
2.6%
2 3
 
2.6%
5 3
 
2.6%
7 3
 
2.6%
19 3
 
2.6%
Other values (64) 76
65.0%
ValueCountFrequency (%)
0 1
 
0.9%
1 1
 
0.9%
2 3
 
2.6%
3 3
 
2.6%
4 3
 
2.6%
5 3
 
2.6%
6 9
7.7%
7 3
 
2.6%
8 4
3.4%
10 3
 
2.6%
ValueCountFrequency (%)
2130 1
0.9%
2080 1
0.9%
2067 1
0.9%
1777 1
0.9%
1740 1
0.9%
1655 1
0.9%
1535 1
0.9%
1498 1
0.9%
1428 1
0.9%
1368 1
0.9%

Interactions

2023-12-12T12:44:17.874553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:08.027030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:09.209994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:10.392488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:11.581826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:13.001390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:14.283254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:15.558153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:16.644194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:18.003191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:08.151575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:09.322100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:10.554460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:12.025386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:13.133352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:14.430625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:15.675093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:16.813593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:18.114403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:08.272160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:09.445975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:10.698216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:12.139122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:13.267602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:14.578226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:15.785590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:16.984464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:18.207570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:08.422349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:09.587913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:10.826014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:12.258474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:13.423404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:14.740442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:15.934527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:17.131542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:18.312239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:08.558514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:09.727851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:10.932671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:12.374959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:13.571554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:14.864771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:16.069189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:17.278007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:18.420357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:08.680890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:09.889899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:11.055498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:12.516867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:13.730072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:14.997768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:16.189697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:17.403741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:18.531930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:08.800928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:10.036417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:11.179278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:12.638469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:13.891859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:15.131901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:16.309975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:17.497316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:18.640069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:08.953109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:10.145869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:11.312899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:12.760844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:14.023018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:15.278123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:16.430232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:17.596510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:18.758271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:09.108669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:10.269497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:11.449563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:12.896079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:14.169422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:15.431340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:16.532383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:17.733635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:44:23.033163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분합계내과질환교통사고(일반)교통사고(오토바이)안전사고화재진화철도사고기타
연도1.0000.1740.5750.5440.3420.1890.6270.3320.4910.447
구분0.1741.0000.7230.6830.6690.6880.7230.6770.0540.639
합계0.5750.7231.0000.6880.7920.9020.9530.9100.9170.822
내과질환0.5440.6830.6881.0000.7780.4570.6340.7190.0000.567
교통사고(일반)0.3420.6690.7920.7781.0000.7340.8120.7540.2630.875
교통사고(오토바이)0.1890.6880.9020.4570.7341.0000.8860.8430.7050.677
안전사고0.6270.7230.9530.6340.8120.8861.0000.8770.8230.798
화재진화0.3320.6770.9100.7190.7540.8430.8771.0000.7870.702
철도사고0.4910.0540.9170.0000.2630.7050.8230.7871.0000.000
기타0.4470.6390.8220.5670.8750.6770.7980.7020.0001.000
2023-12-12T12:44:23.203580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도합계내과질환교통사고(일반)교통사고(오토바이)안전사고화재진화철도사고기타구분
연도1.000-0.073-0.160-0.097-0.3970.0590.052-0.8530.3240.109
합계-0.0731.0000.8130.9520.7590.8110.7410.2410.6550.512
내과질환-0.1600.8131.0000.8530.5450.4820.5800.3000.3720.355
교통사고(일반)-0.0970.9520.8531.0000.7360.7410.7490.2520.6000.488
교통사고(오토바이)-0.3970.7590.5450.7361.0000.6950.6310.4550.5340.475
안전사고0.0590.8110.4820.7410.6951.0000.6980.0370.6990.512
화재진화0.0520.7410.5800.7490.6310.6981.000-0.0220.6940.463
철도사고-0.8530.2410.3000.2520.4550.037-0.0221.000-0.2180.040
기타0.3240.6550.3720.6000.5340.6990.694-0.2181.0000.458
구분0.1090.5120.3550.4880.4750.5120.4630.0400.4581.000

Missing values

2023-12-12T12:44:18.874658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:44:19.042588image/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

연도구분합계내과질환교통사고(일반)교통사고(오토바이)안전사고화재진화철도사고기타
01984유족보상2261154919210157
11984공무상요양1090541311864902787115
21984장해급여42136214061
31985유족보상2491155824181294
41985공무상요양133241192261692164981
51985장해급여801915821296
61986유족보상24910363201611828
71986공무상요양14041192406824163619
81986장해급여731061030098
91987유족보상2831318727181145
연도구분합계내과질환교통사고(일반)교통사고(오토바이)안전사고화재진화철도사고기타
1072019장해급여16740205711030
1082020순직유족보상6948616008
1092020공무상요양621344696028333694001115
1102020장해급여216784211690016
1112021순직유족보상704110021016
1122021공무상요양53775061207023234301298
1132021장해급여20771500670019
1142022순직유족보상109861103306
1152022공무상요양56487221159023148501368
1162022장해급여20492330650014