Overview

Dataset statistics

Number of variables9
Number of observations111
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.8 KiB
Average record size in memory81.2 B

Variable types

Numeric8
Categorical1

Dataset

Description공상 원인별(내과질환,교통사고,안전사고,화재진화,철도사고) 급여심의회 심의가결 추이 데이터입니다. 1984년부터 시작됩니다.
Author공무원연금공단
URLhttps://www.data.go.kr/data/15052928/fileData.do

Alerts

연도 is highly overall correlated with 철도사고High correlation
내과질환 is highly overall correlated with 교통사고(일반) and 2 other fieldsHigh correlation
교통사고(일반) is highly overall correlated with 내과질환 and 4 other fieldsHigh correlation
교통사고(오토바이) is highly overall correlated with 내과질환 and 4 other fieldsHigh correlation
안전사고 is highly overall correlated with 교통사고(일반) and 4 other fieldsHigh correlation
화재진화 is highly overall correlated with 내과질환 and 4 other fieldsHigh correlation
철도사고 is highly overall correlated with 연도High correlation
기타 is highly overall correlated with 교통사고(일반) and 3 other fieldsHigh correlation
구분 is highly overall correlated with 안전사고High correlation
교통사고(오토바이) has 7 (6.3%) zerosZeros
화재진화 has 33 (29.7%) zerosZeros
철도사고 has 37 (33.3%) zerosZeros

Reproduction

Analysis started2023-12-12 13:04:33.477692
Analysis finished2023-12-12 13:04:40.160303
Duration6.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002
Minimum1984
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T22:04:40.233944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1984
5-th percentile1985.5
Q11993
median2002
Q32011
95-th percentile2018.5
Maximum2020
Range36
Interquartile range (IQR)18

Descriptive statistics

Standard deviation10.725501
Coefficient of variation (CV)0.0053573929
Kurtosis-1.2016322
Mean2002
Median Absolute Deviation (MAD)9
Skewness0
Sum222222
Variance115.03636
MonotonicityIncreasing
2023-12-12T22:04:40.375919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1984 3
 
2.7%
2012 3
 
2.7%
2005 3
 
2.7%
2006 3
 
2.7%
2007 3
 
2.7%
2008 3
 
2.7%
2009 3
 
2.7%
2010 3
 
2.7%
2011 3
 
2.7%
2013 3
 
2.7%
Other values (27) 81
73.0%
ValueCountFrequency (%)
1984 3
2.7%
1985 3
2.7%
1986 3
2.7%
1987 3
2.7%
1988 3
2.7%
1989 3
2.7%
1990 3
2.7%
1991 3
2.7%
1992 3
2.7%
1993 3
2.7%
ValueCountFrequency (%)
2020 3
2.7%
2019 3
2.7%
2018 3
2.7%
2017 3
2.7%
2016 3
2.7%
2015 3
2.7%
2014 3
2.7%
2013 3
2.7%
2012 3
2.7%
2011 3
2.7%

구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size1020.0 B
공무상요양
37 
장해급여
37 
유족보상
33 
순직유족보상
4 

Length

Max length6
Median length4
Mean length4.4054054
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공무상요양 37
33.3%
장해급여 37
33.3%
유족보상 33
29.7%
순직유족보상 4
 
3.6%

Length

2023-12-12T22:04:40.565211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:04:40.719651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공무상요양 37
33.3%
장해급여 37
33.3%
유족보상 33
29.7%
순직유족보상 4
 
3.6%

내과질환
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.89189
Minimum9
Maximum446
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T22:04:40.846875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile18
Q141.5
median114
Q3192
95-th percentile349
Maximum446
Range437
Interquartile range (IQR)150.5

Descriptive statistics

Standard deviation106.56209
Coefficient of variation (CV)0.78998144
Kurtosis0.20935869
Mean134.89189
Median Absolute Deviation (MAD)74
Skewness0.94909136
Sum14973
Variance11355.479
MonotonicityNot monotonic
2023-12-12T22:04:40.978905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 4
 
3.6%
26 3
 
2.7%
115 2
 
1.8%
143 2
 
1.8%
108 2
 
1.8%
38 2
 
1.8%
42 2
 
1.8%
46 2
 
1.8%
120 2
 
1.8%
30 2
 
1.8%
Other values (81) 88
79.3%
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 (%)
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%
327 1
0.9%
317 1
0.9%

교통사고(일반)
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215.13514
Minimum5
Maximum960
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T22:04:41.127190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8.5
Q117.5
median49
Q3442.5
95-th percentile776.5
Maximum960
Range955
Interquartile range (IQR)425

Descriptive statistics

Standard deviation284.9134
Coefficient of variation (CV)1.3243462
Kurtosis-0.1486834
Mean215.13514
Median Absolute Deviation (MAD)40
Skewness1.1959882
Sum23880
Variance81175.645
MonotonicityNot monotonic
2023-12-12T22:04:41.275022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 5
 
4.5%
6 4
 
3.6%
16 4
 
3.6%
17 4
 
3.6%
20 2
 
1.8%
21 2
 
1.8%
27 2
 
1.8%
10 2
 
1.8%
85 2
 
1.8%
13 2
 
1.8%
Other values (77) 82
73.9%
ValueCountFrequency (%)
5 1
 
0.9%
6 4
3.6%
8 1
 
0.9%
9 5
4.5%
10 2
 
1.8%
11 1
 
0.9%
12 1
 
0.9%
13 2
 
1.8%
14 2
 
1.8%
15 1
 
0.9%
ValueCountFrequency (%)
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%
752 1
0.9%
737 1
0.9%

교통사고(오토바이)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)54.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.702703
Minimum0
Maximum406
Zeros7
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T22:04:41.416292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median14
Q395.5
95-th percentile284
Maximum406
Range406
Interquartile range (IQR)90.5

Descriptive statistics

Standard deviation99.851489
Coefficient of variation (CV)1.4748523
Kurtosis1.3022081
Mean67.702703
Median Absolute Deviation (MAD)12
Skewness1.5801907
Sum7515
Variance9970.3199
MonotonicityNot monotonic
2023-12-12T22:04:41.534206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
6.3%
2 7
 
6.3%
10 6
 
5.4%
5 6
 
5.4%
4 5
 
4.5%
6 4
 
3.6%
1 4
 
3.6%
13 4
 
3.6%
15 3
 
2.7%
16 3
 
2.7%
Other values (50) 62
55.9%
ValueCountFrequency (%)
0 7
6.3%
1 4
3.6%
2 7
6.3%
3 2
 
1.8%
4 5
4.5%
5 6
5.4%
6 4
3.6%
7 3
2.7%
8 3
2.7%
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
1.8%
280 1
0.9%
263 1
0.9%
261 1
0.9%

안전사고
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean634.30631
Minimum1
Maximum3502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T22:04:41.644120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.5
Q116
median50
Q31111.5
95-th percentile2601
Maximum3502
Range3501
Interquartile range (IQR)1095.5

Descriptive statistics

Standard deviation973.70276
Coefficient of variation (CV)1.5350671
Kurtosis0.83923618
Mean634.30631
Median Absolute Deviation (MAD)42
Skewness1.4239721
Sum70408
Variance948097.07
MonotonicityNot monotonic
2023-12-12T22:04:41.752456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 5
 
4.5%
12 4
 
3.6%
21 3
 
2.7%
18 3
 
2.7%
16 3
 
2.7%
30 3
 
2.7%
58 3
 
2.7%
7 3
 
2.7%
4 3
 
2.7%
50 2
 
1.8%
Other values (71) 79
71.2%
ValueCountFrequency (%)
1 1
 
0.9%
4 3
2.7%
5 2
 
1.8%
6 5
4.5%
7 3
2.7%
8 2
 
1.8%
9 2
 
1.8%
11 2
 
1.8%
12 4
3.6%
13 2
 
1.8%
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%
2247 1
0.9%

화재진화
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.351351
Minimum0
Maximum105
Zeros33
Zeros (%)29.7%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T22:04:41.857716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q330
95-th percentile71
Maximum105
Range105
Interquartile range (IQR)30

Descriptive statistics

Standard deviation26.074595
Coefficient of variation (CV)1.5946447
Kurtosis1.8337272
Mean16.351351
Median Absolute Deviation (MAD)2
Skewness1.6542878
Sum1815
Variance679.88452
MonotonicityNot monotonic
2023-12-12T22:04:41.959535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 33
29.7%
1 15
13.5%
2 14
12.6%
3 9
 
8.1%
16 3
 
2.7%
5 2
 
1.8%
36 2
 
1.8%
33 2
 
1.8%
56 2
 
1.8%
9 2
 
1.8%
Other values (23) 27
24.3%
ValueCountFrequency (%)
0 33
29.7%
1 15
13.5%
2 14
12.6%
3 9
 
8.1%
4 1
 
0.9%
5 2
 
1.8%
9 2
 
1.8%
12 2
 
1.8%
16 3
 
2.7%
21 1
 
0.9%
ValueCountFrequency (%)
105 1
0.9%
100 1
0.9%
97 1
0.9%
77 2
1.8%
75 1
0.9%
67 1
0.9%
66 1
0.9%
65 1
0.9%
62 1
0.9%
59 1
0.9%

철도사고
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8828829
Minimum0
Maximum171
Zeros37
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T22:04:42.056033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q313
95-th percentile38.5
Maximum171
Range171
Interquartile range (IQR)13

Descriptive statistics

Standard deviation20.276157
Coefficient of variation (CV)2.0516439
Kurtosis37.673995
Mean9.8828829
Median Absolute Deviation (MAD)2
Skewness5.3616933
Sum1097
Variance411.12252
MonotonicityNot monotonic
2023-12-12T22:04:42.157749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 37
33.3%
1 10
 
9.0%
2 9
 
8.1%
9 5
 
4.5%
6 4
 
3.6%
4 4
 
3.6%
14 4
 
3.6%
7 4
 
3.6%
13 3
 
2.7%
10 3
 
2.7%
Other values (22) 28
25.2%
ValueCountFrequency (%)
0 37
33.3%
1 10
 
9.0%
2 9
 
8.1%
3 2
 
1.8%
4 4
 
3.6%
5 1
 
0.9%
6 4
 
3.6%
7 4
 
3.6%
9 5
 
4.5%
10 3
 
2.7%
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
1.8%

기타
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299.75676
Minimum0
Maximum2130
Zeros1
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T22:04:42.271202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18
median25
Q3317.5
95-th percentile1595
Maximum2130
Range2130
Interquartile range (IQR)309.5

Descriptive statistics

Standard deviation549.35599
Coefficient of variation (CV)1.8326726
Kurtosis2.8427703
Mean299.75676
Median Absolute Deviation (MAD)19
Skewness2.0038881
Sum33273
Variance301792
MonotonicityNot monotonic
2023-12-12T22:04:42.386872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 8
 
7.2%
8 4
 
3.6%
22 4
 
3.6%
14 4
 
3.6%
2 3
 
2.7%
5 3
 
2.7%
7 3
 
2.7%
10 3
 
2.7%
4 3
 
2.7%
3 3
 
2.7%
Other values (62) 73
65.8%
ValueCountFrequency (%)
0 1
 
0.9%
1 1
 
0.9%
2 3
 
2.7%
3 3
 
2.7%
4 3
 
2.7%
5 3
 
2.7%
6 8
7.2%
7 3
 
2.7%
8 4
3.6%
10 3
 
2.7%
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%
1366 1
0.9%

Interactions

2023-12-12T22:04:39.096454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:33.755574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:34.552928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:35.236160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:35.930188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:36.665461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:37.376818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:38.320222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:39.200466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:33.846756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:34.641029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:35.340153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:36.010079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:36.757563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:37.458930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:38.431999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:39.308004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:33.943687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:34.727663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:35.427151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:36.083527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:36.851102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:37.526989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:38.519186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:39.390211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:34.047158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:34.822880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:35.514024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:36.177172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:36.925654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:37.612706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:38.613295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:39.505541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:34.152958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:34.914127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:35.615888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:36.290189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:37.021684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:37.725367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:38.726190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:39.602375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:34.249398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:35.015974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:35.702775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:36.385911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:37.103672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:37.811147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:38.842226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:39.710612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:34.338025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:35.084557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:35.786562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:36.470155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:37.183944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:38.144406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:38.925882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:39.805504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:34.461564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:35.162037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:35.866282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:36.572597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:37.282572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:38.232245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:04:39.016637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:04:42.696630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분내과질환교통사고(일반)교통사고(오토바이)안전사고화재진화철도사고기타
연도1.0000.2850.6180.1950.1620.6730.1430.4540.352
구분0.2851.0000.5670.7030.7090.7200.6490.0310.632
내과질환0.6180.5671.0000.8720.6500.8160.5500.0000.625
교통사고(일반)0.1950.7030.8721.0000.8840.9170.7550.0000.785
교통사고(오토바이)0.1620.7090.6500.8841.0000.8940.7390.6980.687
안전사고0.6730.7200.8160.9170.8941.0000.7610.8200.803
화재진화0.1430.6490.5500.7550.7390.7611.0000.6490.820
철도사고0.4540.0310.0000.0000.6980.8200.6491.0000.000
기타0.3520.6320.6250.7850.6870.8030.8200.0001.000
2023-12-12T22:04:42.790196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도내과질환교통사고(일반)교통사고(오토바이)안전사고화재진화철도사고기타구분
연도1.000-0.199-0.112-0.3010.0590.054-0.8450.3520.174
내과질환-0.1991.0000.8450.6230.4520.5650.3260.3480.364
교통사고(일반)-0.1120.8451.0000.8340.7230.7610.2670.5910.490
교통사고(오토바이)-0.3010.6230.8341.0000.7800.7040.3990.6020.497
안전사고0.0590.4520.7230.7801.0000.7210.0390.6930.508
화재진화0.0540.5650.7610.7040.7211.000-0.0250.6980.466
철도사고-0.8450.3260.2670.3990.039-0.0251.000-0.2250.017
기타0.3520.3480.5910.6020.6930.698-0.2251.0000.450
구분0.1740.3640.4900.4970.5080.4660.0170.4501.000

Missing values

2023-12-12T22:04:39.932199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:04:40.090954image/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

연도구분내과질환교통사고(일반)교통사고(오토바이)안전사고화재진화철도사고기타
01984유족보상1154919210157
11984공무상요양541311864902787115
21984장해급여136214061
31985유족보상1155824181294
41985공무상요양41192261692164981
51985장해급여1915821296
61986유족보상10363201611828
71986공무상요양1192406824163619
81986장해급여1061030098
91987유족보상1318727181145
연도구분내과질환교통사고(일반)교통사고(오토바이)안전사고화재진화철도사고기타
1012017장해급여27227583020
1022018순직유족보상1214460042
1032018공무상요양1448391703359921322
1042018장해급여29288503022
1052019순직유족보상2910460027
1062019공무상요양16375315635025121428
1072019장해급여40205711030
1082020순직유족보상48616008
1092020공무상요양44696028333694001115
1102020장해급여784211690017