Overview

Dataset statistics

Number of variables20
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.7 KiB
Average record size in memory181.7 B

Variable types

Text1
Numeric19

Dataset

Description한국산업안전보건공단에서 제공하는 산업안전보건에 대한 통계자료로 연도별, 노동지청별 사고로 사망자하신 분들의 수에 대한 데이터 입니다.
URLhttps://www.data.go.kr/data/15084669/fileData.do

Alerts

2004년 사고사망자수 is highly overall correlated with 2005년 사고사망자수 and 17 other fieldsHigh correlation
2005년 사고사망자수 is highly overall correlated with 2004년 사고사망자수 and 17 other fieldsHigh correlation
2006년 사고사망자수 is highly overall correlated with 2004년 사고사망자수 and 17 other fieldsHigh correlation
2007년 사고사망자수 is highly overall correlated with 2004년 사고사망자수 and 17 other fieldsHigh correlation
2008년 사고사망자수 is highly overall correlated with 2004년 사고사망자수 and 17 other fieldsHigh correlation
2009년 사고사망자수 is highly overall correlated with 2004년 사고사망자수 and 17 other fieldsHigh correlation
2010년 사고사망자수 is highly overall correlated with 2004년 사고사망자수 and 17 other fieldsHigh correlation
2011년 사고사망자수 is highly overall correlated with 2004년 사고사망자수 and 17 other fieldsHigh correlation
2012년 사고사망자수 is highly overall correlated with 2004년 사고사망자수 and 17 other fieldsHigh correlation
2013년 사고사망자수 is highly overall correlated with 2004년 사고사망자수 and 17 other fieldsHigh correlation
2014년 사고사망자수 is highly overall correlated with 2004년 사고사망자수 and 17 other fieldsHigh correlation
2015년 사고사망자수 is highly overall correlated with 2004년 사고사망자수 and 17 other fieldsHigh correlation
2016년 사고사망자수 is highly overall correlated with 2004년 사고사망자수 and 17 other fieldsHigh correlation
2017년 사고사망자수 is highly overall correlated with 2004년 사고사망자수 and 17 other fieldsHigh correlation
2018년 사고사망자수 is highly overall correlated with 2004년 사고사망자수 and 17 other fieldsHigh correlation
2019년 사고사망자수 is highly overall correlated with 2004년 사고사망자수 and 17 other fieldsHigh correlation
2020년 사고사망자수 is highly overall correlated with 2004년 사고사망자수 and 17 other fieldsHigh correlation
2021년 사고사망자수 is highly overall correlated with 2004년 사고사망자수 and 17 other fieldsHigh correlation
2022년 사고사망자수 is highly overall correlated with 2004년 사고사망자수 and 17 other fieldsHigh correlation
구분 has unique valuesUnique
2004년 사고사망자수 has 3 (6.1%) zerosZeros
2005년 사고사망자수 has 2 (4.1%) zerosZeros
2006년 사고사망자수 has 1 (2.0%) zerosZeros
2007년 사고사망자수 has 1 (2.0%) zerosZeros
2008년 사고사망자수 has 1 (2.0%) zerosZeros
2009년 사고사망자수 has 1 (2.0%) zerosZeros
2010년 사고사망자수 has 1 (2.0%) zerosZeros
2011년 사고사망자수 has 1 (2.0%) zerosZeros
2012년 사고사망자수 has 1 (2.0%) zerosZeros
2013년 사고사망자수 has 1 (2.0%) zerosZeros
2014년 사고사망자수 has 1 (2.0%) zerosZeros
2015년 사고사망자수 has 1 (2.0%) zerosZeros
2016년 사고사망자수 has 1 (2.0%) zerosZeros
2017년 사고사망자수 has 1 (2.0%) zerosZeros
2018년 사고사망자수 has 1 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-12 02:26:33.249097
Analysis finished2023-12-12 02:27:15.152398
Duration41.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-12T11:27:15.302637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.4489796
Min length4

Characters and Unicode

Total characters267
Distinct characters51
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

Unique49 ?
Unique (%)100.0%

Sample

1st row서 울 청
2nd row서울강남
3rd row서울동부
4th row서울서부
5th row서울남부
ValueCountFrequency (%)
8
 
8.4%
7
 
7.4%
7
 
7.4%
4
 
4.2%
4
 
4.2%
3
 
3.2%
3
 
3.2%
3
 
3.2%
2
 
2.1%
2
 
2.1%
Other values (46) 52
54.7%
2023-12-12T11:27:15.690771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
142
53.2%
14
 
5.2%
10
 
3.7%
9
 
3.4%
8
 
3.0%
8
 
3.0%
7
 
2.6%
4
 
1.5%
3
 
1.1%
3
 
1.1%
Other values (41) 59
22.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 142
53.2%
Other Letter 125
46.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
14
 
11.2%
10
 
8.0%
9
 
7.2%
8
 
6.4%
8
 
6.4%
7
 
5.6%
4
 
3.2%
3
 
2.4%
3
 
2.4%
3
 
2.4%
Other values (40) 56
44.8%
Space Separator
ValueCountFrequency (%)
142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 142
53.2%
Hangul 125
46.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
14
 
11.2%
10
 
8.0%
9
 
7.2%
8
 
6.4%
8
 
6.4%
7
 
5.6%
4
 
3.2%
3
 
2.4%
3
 
2.4%
3
 
2.4%
Other values (40) 56
44.8%
Common
ValueCountFrequency (%)
142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 142
53.2%
Hangul 125
46.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
142
100.0%
Hangul
ValueCountFrequency (%)
14
 
11.2%
10
 
8.0%
9
 
7.2%
8
 
6.4%
8
 
6.4%
7
 
5.6%
4
 
3.2%
3
 
2.4%
3
 
2.4%
3
 
2.4%
Other values (40) 56
44.8%

2004년 사고사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.489796
Minimum0
Maximum80
Zeros3
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T11:27:15.872114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.8
Q112
median24
Q338
95-th percentile47.6
Maximum80
Range80
Interquartile range (IQR)26

Descriptive statistics

Standard deviation17.85138
Coefficient of variation (CV)0.67389647
Kurtosis1.3242784
Mean26.489796
Median Absolute Deviation (MAD)13
Skewness0.95351263
Sum1298
Variance318.67177
MonotonicityNot monotonic
2023-12-12T11:27:16.019883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
11 3
 
6.1%
0 3
 
6.1%
47 2
 
4.1%
38 2
 
4.1%
34 2
 
4.1%
40 2
 
4.1%
46 2
 
4.1%
12 2
 
4.1%
25 2
 
4.1%
42 2
 
4.1%
Other values (22) 27
55.1%
ValueCountFrequency (%)
0 3
6.1%
7 1
 
2.0%
8 2
4.1%
9 1
 
2.0%
10 2
4.1%
11 3
6.1%
12 2
4.1%
13 1
 
2.0%
15 1
 
2.0%
16 1
 
2.0%
ValueCountFrequency (%)
80 1
2.0%
79 1
2.0%
48 1
2.0%
47 2
4.1%
46 2
4.1%
45 1
2.0%
42 2
4.1%
40 2
4.1%
38 2
4.1%
37 1
2.0%

2005년 사고사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.22449
Minimum0
Maximum65
Zeros2
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T11:27:16.186430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.2
Q114
median21
Q334
95-th percentile52
Maximum65
Range65
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.338563
Coefficient of variation (CV)0.59190361
Kurtosis0.37561354
Mean24.22449
Median Absolute Deviation (MAD)9
Skewness0.730877
Sum1187
Variance205.59439
MonotonicityNot monotonic
2023-12-12T11:27:16.373537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
17 3
 
6.1%
13 3
 
6.1%
22 2
 
4.1%
9 2
 
4.1%
0 2
 
4.1%
32 2
 
4.1%
20 2
 
4.1%
37 2
 
4.1%
12 2
 
4.1%
34 2
 
4.1%
Other values (24) 27
55.1%
ValueCountFrequency (%)
0 2
4.1%
4 1
 
2.0%
7 1
 
2.0%
9 2
4.1%
11 1
 
2.0%
12 2
4.1%
13 3
6.1%
14 1
 
2.0%
15 2
4.1%
16 1
 
2.0%
ValueCountFrequency (%)
65 1
2.0%
55 1
2.0%
54 1
2.0%
49 1
2.0%
43 1
2.0%
40 1
2.0%
39 1
2.0%
37 2
4.1%
36 1
2.0%
35 1
2.0%

2006년 사고사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.795918
Minimum0
Maximum53
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T11:27:16.530250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.8
Q113
median22
Q331
95-th percentile42.6
Maximum53
Range53
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.315268
Coefficient of variation (CV)0.54024003
Kurtosis-0.28591373
Mean22.795918
Median Absolute Deviation (MAD)9
Skewness0.32159366
Sum1117
Variance151.66582
MonotonicityNot monotonic
2023-12-12T11:27:16.681958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
22 5
 
10.2%
18 3
 
6.1%
7 3
 
6.1%
26 3
 
6.1%
29 3
 
6.1%
31 3
 
6.1%
16 2
 
4.1%
10 2
 
4.1%
19 2
 
4.1%
42 2
 
4.1%
Other values (20) 21
42.9%
ValueCountFrequency (%)
0 1
 
2.0%
3 1
 
2.0%
4 1
 
2.0%
6 1
 
2.0%
7 3
6.1%
10 2
4.1%
11 1
 
2.0%
12 1
 
2.0%
13 2
4.1%
14 1
 
2.0%
ValueCountFrequency (%)
53 1
 
2.0%
49 1
 
2.0%
43 1
 
2.0%
42 2
4.1%
38 1
 
2.0%
37 1
 
2.0%
36 1
 
2.0%
33 1
 
2.0%
32 1
 
2.0%
31 3
6.1%

2007년 사고사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.183673
Minimum0
Maximum59
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T11:27:16.856471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.2
Q114
median22
Q332
95-th percentile41
Maximum59
Range59
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.477769
Coefficient of variation (CV)0.53821364
Kurtosis-0.068282519
Mean23.183673
Median Absolute Deviation (MAD)9
Skewness0.46856662
Sum1136
Variance155.69473
MonotonicityNot monotonic
2023-12-12T11:27:17.022767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
22 5
 
10.2%
15 5
 
10.2%
11 4
 
8.2%
41 3
 
6.1%
30 2
 
4.1%
40 2
 
4.1%
8 2
 
4.1%
10 2
 
4.1%
36 2
 
4.1%
24 2
 
4.1%
Other values (19) 20
40.8%
ValueCountFrequency (%)
0 1
 
2.0%
4 1
 
2.0%
5 1
 
2.0%
8 2
 
4.1%
10 2
 
4.1%
11 4
8.2%
13 1
 
2.0%
14 1
 
2.0%
15 5
10.2%
17 1
 
2.0%
ValueCountFrequency (%)
59 1
 
2.0%
42 1
 
2.0%
41 3
6.1%
40 2
4.1%
38 1
 
2.0%
36 2
4.1%
35 1
 
2.0%
34 1
 
2.0%
32 1
 
2.0%
30 2
4.1%

2008년 사고사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.918367
Minimum0
Maximum74
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T11:27:17.175375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.8
Q112
median21
Q332
95-th percentile53.8
Maximum74
Range74
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.884789
Coefficient of variation (CV)0.66412515
Kurtosis1.7090699
Mean23.918367
Median Absolute Deviation (MAD)9
Skewness1.1778569
Sum1172
Variance252.32653
MonotonicityNot monotonic
2023-12-12T11:27:17.324856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
12 3
 
6.1%
19 3
 
6.1%
25 3
 
6.1%
9 2
 
4.1%
36 2
 
4.1%
21 2
 
4.1%
28 2
 
4.1%
20 2
 
4.1%
18 2
 
4.1%
10 2
 
4.1%
Other values (26) 26
53.1%
ValueCountFrequency (%)
0 1
 
2.0%
2 1
 
2.0%
4 1
 
2.0%
6 1
 
2.0%
7 1
 
2.0%
8 1
 
2.0%
9 2
4.1%
10 2
4.1%
11 1
 
2.0%
12 3
6.1%
ValueCountFrequency (%)
74 1
2.0%
68 1
2.0%
55 1
2.0%
52 1
2.0%
42 1
2.0%
40 1
2.0%
39 1
2.0%
38 1
2.0%
37 1
2.0%
36 2
4.1%

2009년 사고사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.183673
Minimum0
Maximum62
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T11:27:17.512702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q113
median21
Q331
95-th percentile47.4
Maximum62
Range62
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.106413
Coefficient of variation (CV)0.56532943
Kurtosis0.40616578
Mean23.183673
Median Absolute Deviation (MAD)9
Skewness0.66938068
Sum1136
Variance171.77806
MonotonicityNot monotonic
2023-12-12T11:27:17.662172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
8 4
 
8.2%
28 3
 
6.1%
34 3
 
6.1%
24 2
 
4.1%
35 2
 
4.1%
19 2
 
4.1%
25 2
 
4.1%
17 2
 
4.1%
7 2
 
4.1%
49 2
 
4.1%
Other values (21) 25
51.0%
ValueCountFrequency (%)
0 1
 
2.0%
5 1
 
2.0%
7 2
4.1%
8 4
8.2%
10 1
 
2.0%
11 2
4.1%
12 1
 
2.0%
13 1
 
2.0%
14 1
 
2.0%
15 2
4.1%
ValueCountFrequency (%)
62 1
 
2.0%
49 2
4.1%
45 1
 
2.0%
41 1
 
2.0%
38 1
 
2.0%
35 2
4.1%
34 3
6.1%
33 1
 
2.0%
31 1
 
2.0%
30 1
 
2.0%

2010년 사고사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.734694
Minimum0
Maximum51
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T11:27:17.812538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.8
Q113
median22
Q331
95-th percentile40.6
Maximum51
Range51
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.773515
Coefficient of variation (CV)0.51786554
Kurtosis-0.63096995
Mean22.734694
Median Absolute Deviation (MAD)9
Skewness0.22179897
Sum1114
Variance138.61565
MonotonicityNot monotonic
2023-12-12T11:27:17.945626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
13 3
 
6.1%
7 3
 
6.1%
30 3
 
6.1%
25 3
 
6.1%
19 3
 
6.1%
31 2
 
4.1%
40 2
 
4.1%
23 2
 
4.1%
22 2
 
4.1%
14 2
 
4.1%
Other values (21) 24
49.0%
ValueCountFrequency (%)
0 1
 
2.0%
4 1
 
2.0%
5 1
 
2.0%
7 3
6.1%
10 1
 
2.0%
11 1
 
2.0%
12 2
4.1%
13 3
6.1%
14 2
4.1%
15 2
4.1%
ValueCountFrequency (%)
51 1
2.0%
42 1
2.0%
41 1
2.0%
40 2
4.1%
39 1
2.0%
37 1
2.0%
36 2
4.1%
35 1
2.0%
34 1
2.0%
31 2
4.1%

2011년 사고사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.040816
Minimum0
Maximum51
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T11:27:18.076217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.8
Q114
median21
Q329
95-th percentile44.6
Maximum51
Range51
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.579678
Coefficient of variation (CV)0.54597363
Kurtosis-0.56427804
Mean23.040816
Median Absolute Deviation (MAD)8
Skewness0.44563105
Sum1129
Variance158.2483
MonotonicityNot monotonic
2023-12-12T11:27:18.202820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
28 4
 
8.2%
12 3
 
6.1%
21 3
 
6.1%
15 3
 
6.1%
14 3
 
6.1%
25 2
 
4.1%
16 2
 
4.1%
36 2
 
4.1%
42 2
 
4.1%
13 2
 
4.1%
Other values (20) 23
46.9%
ValueCountFrequency (%)
0 1
 
2.0%
2 1
 
2.0%
5 1
 
2.0%
7 1
 
2.0%
10 2
4.1%
11 1
 
2.0%
12 3
6.1%
13 2
4.1%
14 3
6.1%
15 3
6.1%
ValueCountFrequency (%)
51 1
2.0%
48 1
2.0%
45 1
2.0%
44 1
2.0%
42 2
4.1%
41 1
2.0%
40 1
2.0%
36 2
4.1%
35 1
2.0%
33 1
2.0%

2012년 사고사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.142857
Minimum0
Maximum54
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T11:27:18.357709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.4
Q114
median21
Q333
95-th percentile45.2
Maximum54
Range54
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.342339
Coefficient of variation (CV)0.53331095
Kurtosis-0.25611514
Mean23.142857
Median Absolute Deviation (MAD)9
Skewness0.59878665
Sum1134
Variance152.33333
MonotonicityNot monotonic
2023-12-12T11:27:18.497163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
21 4
 
8.2%
14 3
 
6.1%
13 3
 
6.1%
33 3
 
6.1%
9 3
 
6.1%
17 2
 
4.1%
30 2
 
4.1%
19 2
 
4.1%
16 2
 
4.1%
35 2
 
4.1%
Other values (18) 23
46.9%
ValueCountFrequency (%)
0 1
 
2.0%
8 2
4.1%
9 3
6.1%
10 1
 
2.0%
11 2
4.1%
13 3
6.1%
14 3
6.1%
15 1
 
2.0%
16 2
4.1%
17 2
4.1%
ValueCountFrequency (%)
54 1
 
2.0%
50 1
 
2.0%
46 1
 
2.0%
44 2
4.1%
37 1
 
2.0%
36 1
 
2.0%
35 2
4.1%
34 1
 
2.0%
33 3
6.1%
32 1
 
2.0%

2013년 사고사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.244898
Minimum0
Maximum53
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T11:27:18.664950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q111
median22
Q331
95-th percentile47.8
Maximum53
Range53
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.3768
Coefficient of variation (CV)0.60134238
Kurtosis-0.38196273
Mean22.244898
Median Absolute Deviation (MAD)11
Skewness0.58435811
Sum1090
Variance178.93878
MonotonicityNot monotonic
2023-12-12T11:27:19.117606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
23 5
 
10.2%
22 3
 
6.1%
5 3
 
6.1%
14 2
 
4.1%
8 2
 
4.1%
51 2
 
4.1%
11 2
 
4.1%
37 2
 
4.1%
24 2
 
4.1%
7 2
 
4.1%
Other values (21) 24
49.0%
ValueCountFrequency (%)
0 1
 
2.0%
5 3
6.1%
7 2
4.1%
8 2
4.1%
9 2
4.1%
10 1
 
2.0%
11 2
4.1%
12 2
4.1%
14 2
4.1%
15 1
 
2.0%
ValueCountFrequency (%)
53 1
2.0%
51 2
4.1%
43 1
2.0%
42 1
2.0%
41 1
2.0%
38 1
2.0%
37 2
4.1%
36 1
2.0%
35 1
2.0%
33 1
2.0%

2014년 사고사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.244898
Minimum0
Maximum41
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T11:27:19.270028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.4
Q113
median19
Q330
95-th percentile36.6
Maximum41
Range41
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.223605
Coefficient of variation (CV)0.50499664
Kurtosis-0.73876197
Mean20.244898
Median Absolute Deviation (MAD)8
Skewness0.15421642
Sum992
Variance104.52211
MonotonicityNot monotonic
2023-12-12T11:27:19.411529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
31 4
 
8.2%
17 3
 
6.1%
16 3
 
6.1%
8 3
 
6.1%
19 3
 
6.1%
32 3
 
6.1%
7 2
 
4.1%
24 2
 
4.1%
41 2
 
4.1%
14 2
 
4.1%
Other values (18) 22
44.9%
ValueCountFrequency (%)
0 1
 
2.0%
2 1
 
2.0%
6 1
 
2.0%
7 2
4.1%
8 3
6.1%
10 1
 
2.0%
11 2
4.1%
12 1
 
2.0%
13 1
 
2.0%
14 2
4.1%
ValueCountFrequency (%)
41 2
4.1%
37 1
 
2.0%
36 1
 
2.0%
32 3
6.1%
31 4
8.2%
30 2
4.1%
29 1
 
2.0%
27 1
 
2.0%
26 1
 
2.0%
25 1
 
2.0%

2015년 사고사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.489796
Minimum0
Maximum48
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T11:27:19.548741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.8
Q112
median17
Q327
95-th percentile39.2
Maximum48
Range48
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.514122
Coefficient of variation (CV)0.53946806
Kurtosis0.050557992
Mean19.489796
Median Absolute Deviation (MAD)6
Skewness0.71461262
Sum955
Variance110.54677
MonotonicityNot monotonic
2023-12-12T11:27:19.680094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
14 6
 
12.2%
12 5
 
10.2%
28 3
 
6.1%
17 3
 
6.1%
26 3
 
6.1%
11 2
 
4.1%
18 2
 
4.1%
5 2
 
4.1%
13 2
 
4.1%
0 1
 
2.0%
Other values (20) 20
40.8%
ValueCountFrequency (%)
0 1
 
2.0%
5 2
 
4.1%
7 1
 
2.0%
8 1
 
2.0%
9 1
 
2.0%
10 1
 
2.0%
11 2
 
4.1%
12 5
10.2%
13 2
 
4.1%
14 6
12.2%
ValueCountFrequency (%)
48 1
 
2.0%
41 1
 
2.0%
40 1
 
2.0%
38 1
 
2.0%
37 1
 
2.0%
32 1
 
2.0%
31 1
 
2.0%
30 1
 
2.0%
29 1
 
2.0%
28 3
6.1%

2016년 사고사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.77551
Minimum0
Maximum45
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T11:27:19.830664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.8
Q112
median18
Q328
95-th percentile38.6
Maximum45
Range45
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.150981
Coefficient of variation (CV)0.56387832
Kurtosis-0.53858348
Mean19.77551
Median Absolute Deviation (MAD)9
Skewness0.45736041
Sum969
Variance124.34439
MonotonicityNot monotonic
2023-12-12T11:27:19.976666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
18 5
 
10.2%
13 4
 
8.2%
28 4
 
8.2%
14 3
 
6.1%
8 2
 
4.1%
6 2
 
4.1%
24 2
 
4.1%
23 2
 
4.1%
9 2
 
4.1%
12 2
 
4.1%
Other values (20) 21
42.9%
ValueCountFrequency (%)
0 1
 
2.0%
3 1
 
2.0%
4 1
 
2.0%
6 2
4.1%
7 1
 
2.0%
8 2
4.1%
9 2
4.1%
10 1
 
2.0%
12 2
4.1%
13 4
8.2%
ValueCountFrequency (%)
45 1
2.0%
44 1
2.0%
39 1
2.0%
38 1
2.0%
37 1
2.0%
35 1
2.0%
34 1
2.0%
32 1
2.0%
31 1
2.0%
30 1
2.0%

2017년 사고사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.673469
Minimum0
Maximum50
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T11:27:20.126651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.4
Q113
median19
Q325
95-th percentile37.2
Maximum50
Range50
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.595572
Coefficient of variation (CV)0.53857163
Kurtosis0.80449925
Mean19.673469
Median Absolute Deviation (MAD)6
Skewness0.68312552
Sum964
Variance112.26616
MonotonicityNot monotonic
2023-12-12T11:27:20.294336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
15 4
 
8.2%
22 4
 
8.2%
20 3
 
6.1%
16 3
 
6.1%
33 2
 
4.1%
28 2
 
4.1%
12 2
 
4.1%
4 2
 
4.1%
25 2
 
4.1%
18 2
 
4.1%
Other values (19) 23
46.9%
ValueCountFrequency (%)
0 1
2.0%
4 2
4.1%
5 1
2.0%
6 2
4.1%
8 2
4.1%
10 1
2.0%
11 1
2.0%
12 2
4.1%
13 1
2.0%
14 1
2.0%
ValueCountFrequency (%)
50 1
2.0%
47 1
2.0%
38 1
2.0%
36 1
2.0%
33 2
4.1%
30 1
2.0%
29 1
2.0%
28 2
4.1%
27 1
2.0%
26 1
2.0%

2018년 사고사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.816327
Minimum0
Maximum51
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T11:27:20.486029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.2
Q112
median18
Q325
95-th percentile41.8
Maximum51
Range51
Interquartile range (IQR)13

Descriptive statistics

Standard deviation11.51932
Coefficient of variation (CV)0.5813045
Kurtosis0.36067145
Mean19.816327
Median Absolute Deviation (MAD)7
Skewness0.7615533
Sum971
Variance132.69473
MonotonicityNot monotonic
2023-12-12T11:27:20.656034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
18 4
 
8.2%
16 3
 
6.1%
8 3
 
6.1%
7 3
 
6.1%
22 3
 
6.1%
13 2
 
4.1%
23 2
 
4.1%
15 2
 
4.1%
24 2
 
4.1%
27 2
 
4.1%
Other values (20) 23
46.9%
ValueCountFrequency (%)
0 1
 
2.0%
3 1
 
2.0%
4 1
 
2.0%
7 3
6.1%
8 3
6.1%
9 2
4.1%
10 1
 
2.0%
12 1
 
2.0%
13 2
4.1%
14 2
4.1%
ValueCountFrequency (%)
51 1
2.0%
47 1
2.0%
43 1
2.0%
40 1
2.0%
37 1
2.0%
35 1
2.0%
34 1
2.0%
32 1
2.0%
30 1
2.0%
27 2
4.1%

2019년 사고사망자수
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.44898
Minimum2
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T11:27:20.827910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.4
Q110
median16
Q324
95-th percentile33.6
Maximum46
Range44
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.206332
Coefficient of variation (CV)0.5849243
Kurtosis0.47894167
Mean17.44898
Median Absolute Deviation (MAD)7
Skewness0.8610166
Sum855
Variance104.16922
MonotonicityNot monotonic
2023-12-12T11:27:21.027521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
7 3
 
6.1%
19 3
 
6.1%
10 3
 
6.1%
12 3
 
6.1%
18 3
 
6.1%
17 2
 
4.1%
24 2
 
4.1%
8 2
 
4.1%
33 2
 
4.1%
25 2
 
4.1%
Other values (20) 24
49.0%
ValueCountFrequency (%)
2 1
 
2.0%
4 2
4.1%
5 1
 
2.0%
6 1
 
2.0%
7 3
6.1%
8 2
4.1%
9 2
4.1%
10 3
6.1%
11 1
 
2.0%
12 3
6.1%
ValueCountFrequency (%)
46 1
2.0%
44 1
2.0%
34 1
2.0%
33 2
4.1%
31 1
2.0%
30 1
2.0%
29 1
2.0%
27 1
2.0%
25 2
4.1%
24 2
4.1%

2020년 사고사망자수
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18
Minimum3
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T11:27:21.192416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4.4
Q111
median17
Q323
95-th percentile32.6
Maximum67
Range64
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.472794
Coefficient of variation (CV)0.63737744
Kurtosis6.0740716
Mean18
Median Absolute Deviation (MAD)6
Skewness1.8518685
Sum882
Variance131.625
MonotonicityNot monotonic
2023-12-12T11:27:21.346384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
17 4
 
8.2%
12 3
 
6.1%
21 3
 
6.1%
22 3
 
6.1%
7 3
 
6.1%
9 2
 
4.1%
24 2
 
4.1%
13 2
 
4.1%
28 2
 
4.1%
26 2
 
4.1%
Other values (18) 23
46.9%
ValueCountFrequency (%)
3 2
4.1%
4 1
 
2.0%
5 1
 
2.0%
6 2
4.1%
7 3
6.1%
9 2
4.1%
10 1
 
2.0%
11 2
4.1%
12 3
6.1%
13 2
4.1%
ValueCountFrequency (%)
67 1
2.0%
46 1
2.0%
33 1
2.0%
32 1
2.0%
29 1
2.0%
28 2
4.1%
27 1
2.0%
26 2
4.1%
24 2
4.1%
23 1
2.0%

2021년 사고사망자수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.897959
Minimum4
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T11:27:21.543321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q110
median16
Q322
95-th percentile30.6
Maximum55
Range51
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.7920991
Coefficient of variation (CV)0.57948412
Kurtosis3.6742109
Mean16.897959
Median Absolute Deviation (MAD)6
Skewness1.4289171
Sum828
Variance95.885204
MonotonicityNot monotonic
2023-12-12T11:27:21.705072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
22 6
 
12.2%
12 5
 
10.2%
16 4
 
8.2%
7 3
 
6.1%
20 3
 
6.1%
5 3
 
6.1%
25 2
 
4.1%
10 2
 
4.1%
13 2
 
4.1%
15 2
 
4.1%
Other values (15) 17
34.7%
ValueCountFrequency (%)
4 1
 
2.0%
5 3
6.1%
6 2
 
4.1%
7 3
6.1%
8 2
 
4.1%
9 1
 
2.0%
10 2
 
4.1%
12 5
10.2%
13 2
 
4.1%
14 1
 
2.0%
ValueCountFrequency (%)
55 1
 
2.0%
40 1
 
2.0%
31 1
 
2.0%
30 1
 
2.0%
29 1
 
2.0%
26 1
 
2.0%
25 2
 
4.1%
24 1
 
2.0%
23 1
 
2.0%
22 6
12.2%

2022년 사고사망자수
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.836735
Minimum3
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T11:27:21.865661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q110
median15
Q323
95-th percentile35
Maximum67
Range64
Interquartile range (IQR)13

Descriptive statistics

Standard deviation11.878459
Coefficient of variation (CV)0.6659548
Kurtosis5.2385965
Mean17.836735
Median Absolute Deviation (MAD)5
Skewness1.8960209
Sum874
Variance141.09779
MonotonicityNot monotonic
2023-12-12T11:27:22.070709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
13 5
 
10.2%
11 4
 
8.2%
15 4
 
8.2%
5 3
 
6.1%
10 3
 
6.1%
16 3
 
6.1%
32 3
 
6.1%
8 3
 
6.1%
28 2
 
4.1%
18 2
 
4.1%
Other values (16) 17
34.7%
ValueCountFrequency (%)
3 1
 
2.0%
5 3
6.1%
6 1
 
2.0%
7 1
 
2.0%
8 3
6.1%
9 1
 
2.0%
10 3
6.1%
11 4
8.2%
13 5
10.2%
14 1
 
2.0%
ValueCountFrequency (%)
67 1
 
2.0%
47 1
 
2.0%
37 1
 
2.0%
32 3
6.1%
30 2
4.1%
28 2
4.1%
26 1
 
2.0%
24 1
 
2.0%
23 1
 
2.0%
21 1
 
2.0%

Interactions

2023-12-12T11:27:12.523145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:33.889936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T11:26:56.385837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:58.590672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:00.645908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:02.932269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:05.316530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:07.387074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:09.469531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:11.566754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:13.980571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:35.109819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:37.649218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:39.720507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:41.862891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:44.338130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:46.513236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:48.491404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:50.822496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:52.680289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:54.551554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:56.494938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:58.670629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:00.762396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:03.041123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:05.417936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:07.487500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:09.593433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:11.665247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:14.086895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:35.241575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:37.757340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:39.833055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:41.963031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:44.456149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:46.617930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:48.587183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:50.924952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:52.784080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:54.632688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:56.600804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:58.763089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:00.884621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:03.138536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:05.554615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:07.595697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:09.688926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:11.769142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:14.189024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:35.700922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:37.860189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:39.926101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:42.058187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:44.578350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:46.720530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:48.682453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:51.013995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:52.872227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:54.725705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:56.705873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:58.862617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:00.990846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:03.221486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:05.683213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:07.699256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:09.792519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:11.862289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:14.273372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:35.815840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:37.951559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:40.019468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:42.169967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:44.687280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:46.836243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:48.789133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:51.111172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:52.961870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:54.825122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:56.805180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:58.956883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:01.117499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:03.332050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:05.786422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:07.810678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:09.904764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:11.973986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:14.356891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:35.928902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:38.059168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:40.122376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:42.288735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:44.795847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:46.934620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:48.885829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:51.230855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:53.052853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:54.955088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:56.902924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:59.053973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:01.238759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:03.442087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:05.889353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:07.908304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:10.013244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:12.090914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:14.437670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:36.032779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:38.156888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:40.210431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:42.402407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:44.909124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:47.031534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:48.998916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:51.333632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:53.145331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:55.055994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:57.003879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:59.164682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:01.353093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:03.540349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:06.016580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:08.020353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:10.130176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:12.195061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:14.548659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:36.166784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:38.254809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:40.319014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:42.516144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:45.032580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:47.140178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:49.121727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:51.435125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:53.246150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:55.166637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:57.097545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:59.283685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:01.469194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:03.659155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:06.126069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:08.151471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:10.250461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:12.300340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:14.635019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:36.269114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:38.339550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:40.425542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:42.919896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:45.129699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:47.232771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:49.228282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:51.515126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:53.344966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:55.265733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:57.473979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:26:59.392233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:01.580421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:03.761602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:06.223693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:08.243359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:10.378375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:12.411777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:27:22.215775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분2004년 사고사망자수2005년 사고사망자수2006년 사고사망자수2007년 사고사망자수2008년 사고사망자수2009년 사고사망자수2010년 사고사망자수2011년 사고사망자수2012년 사고사망자수2013년 사고사망자수2014년 사고사망자수2015년 사고사망자수2016년 사고사망자수2017년 사고사망자수2018년 사고사망자수2019년 사고사망자수2020년 사고사망자수2021년 사고사망자수2022년 사고사망자수
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2004년 사고사망자수1.0001.0000.7540.5520.7450.5880.7240.7390.4850.6400.4720.6670.6930.7030.7220.6010.4660.8390.6850.641
2005년 사고사망자수1.0000.7541.0000.6730.6920.7080.6660.8320.8020.5630.6810.8270.7830.7710.7250.9020.6650.7260.7450.787
2006년 사고사망자수1.0000.5520.6731.0000.5870.6340.5500.7240.7760.5260.7870.8700.8390.8660.6200.8330.6060.7640.7010.667
2007년 사고사망자수1.0000.7450.6920.5871.0000.7050.9030.7660.6870.8420.6520.6550.7840.7600.7020.6610.7360.7860.7350.664
2008년 사고사망자수1.0000.5880.7080.6340.7051.0000.7000.7310.7060.7230.7040.5600.7850.6960.5710.7060.6300.7520.8410.883
2009년 사고사망자수1.0000.7240.6660.5500.9030.7001.0000.7750.7380.8790.7190.5970.7790.7220.8400.7270.8210.7910.7330.756
2010년 사고사망자수1.0000.7390.8320.7240.7660.7310.7751.0000.8460.6960.6900.7030.8940.8330.6330.8870.6330.7700.7410.643
2011년 사고사망자수1.0000.4850.8020.7760.6870.7060.7380.8461.0000.6920.6900.7520.8790.7310.6230.8280.5970.7730.7030.637
2012년 사고사망자수1.0000.6400.5630.5260.8420.7230.8790.6960.6921.0000.6110.6720.7810.4260.7390.5870.7170.7590.5420.641
2013년 사고사망자수1.0000.4720.6810.7870.6520.7040.7190.6900.6900.6111.0000.8680.7880.8670.7270.7800.6070.8190.6150.732
2014년 사고사망자수1.0000.6670.8270.8700.6550.5600.5970.7030.7520.6720.8681.0000.8000.8170.6950.7790.6840.7050.7540.747
2015년 사고사망자수1.0000.6930.7830.8390.7840.7850.7790.8940.8790.7810.7880.8001.0000.7780.6530.8530.6900.8120.7560.674
2016년 사고사망자수1.0000.7030.7710.8660.7600.6960.7220.8330.7310.4260.8670.8170.7781.0000.6140.8220.4060.6500.6330.636
2017년 사고사망자수1.0000.7220.7250.6200.7020.5710.8400.6330.6230.7390.7270.6950.6530.6141.0000.5390.7280.7130.7010.671
2018년 사고사망자수1.0000.6010.9020.8330.6610.7060.7270.8870.8280.5870.7800.7790.8530.8220.5391.0000.6410.8150.7120.736
2019년 사고사망자수1.0000.4660.6650.6060.7360.6300.8210.6330.5970.7170.6070.6840.6900.4060.7280.6411.0000.7180.6700.710
2020년 사고사망자수1.0000.8390.7260.7640.7860.7520.7910.7700.7730.7590.8190.7050.8120.6500.7130.8150.7181.0000.8000.848
2021년 사고사망자수1.0000.6850.7450.7010.7350.8410.7330.7410.7030.5420.6150.7540.7560.6330.7010.7120.6700.8001.0000.935
2022년 사고사망자수1.0000.6410.7870.6670.6640.8830.7560.6430.6370.6410.7320.7470.6740.6360.6710.7360.7100.8480.9351.000
2023-12-12T11:27:22.453142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2004년 사고사망자수2005년 사고사망자수2006년 사고사망자수2007년 사고사망자수2008년 사고사망자수2009년 사고사망자수2010년 사고사망자수2011년 사고사망자수2012년 사고사망자수2013년 사고사망자수2014년 사고사망자수2015년 사고사망자수2016년 사고사망자수2017년 사고사망자수2018년 사고사망자수2019년 사고사망자수2020년 사고사망자수2021년 사고사망자수2022년 사고사망자수
2004년 사고사망자수1.0000.8710.7240.7070.6810.7230.7690.6330.6520.7250.6800.6660.7070.6640.5820.5300.6350.5080.568
2005년 사고사망자수0.8711.0000.7820.7830.7450.7940.7690.7150.7270.7440.7290.7280.7840.7220.7190.6460.7260.6500.669
2006년 사고사망자수0.7240.7821.0000.8500.7530.7820.8000.8130.7930.8250.8520.8140.8430.8010.8230.8150.8070.7520.725
2007년 사고사망자수0.7070.7830.8501.0000.8420.8580.8790.8690.8350.8580.8200.8450.8450.7760.8350.7920.8530.7800.751
2008년 사고사망자수0.6810.7450.7530.8421.0000.8280.8410.8270.8460.7690.7680.8000.7980.8010.7490.7530.8090.7290.706
2009년 사고사망자수0.7230.7940.7820.8580.8281.0000.8360.8590.8790.8310.8320.7770.8110.8310.8180.7760.8230.7890.734
2010년 사고사망자수0.7690.7690.8000.8790.8410.8361.0000.8010.8250.8010.8060.8200.8110.8100.8040.7750.8350.7650.705
2011년 사고사망자수0.6330.7150.8130.8690.8270.8590.8011.0000.8470.8620.8490.8520.8250.8330.8560.8300.8710.8150.810
2012년 사고사망자수0.6520.7270.7930.8350.8460.8790.8250.8471.0000.8230.8960.8050.8130.8600.8380.7830.8600.7950.731
2013년 사고사망자수0.7250.7440.8250.8580.7690.8310.8010.8620.8231.0000.8940.8020.8340.8150.7860.7450.8550.7620.714
2014년 사고사망자수0.6800.7290.8520.8200.7680.8320.8060.8490.8960.8941.0000.7870.8340.8240.8620.8010.8740.8080.734
2015년 사고사망자수0.6660.7280.8140.8450.8000.7770.8200.8520.8050.8020.7871.0000.7970.8060.8550.7640.8310.7540.725
2016년 사고사망자수0.7070.7840.8430.8450.7980.8110.8110.8250.8130.8340.8340.7971.0000.8350.8490.7930.7970.7390.690
2017년 사고사망자수0.6640.7220.8010.7760.8010.8310.8100.8330.8600.8150.8240.8060.8351.0000.8130.7470.8780.7080.701
2018년 사고사망자수0.5820.7190.8230.8350.7490.8180.8040.8560.8380.7860.8620.8550.8490.8131.0000.8750.8740.8280.795
2019년 사고사망자수0.5300.6460.8150.7920.7530.7760.7750.8300.7830.7450.8010.7640.7930.7470.8751.0000.8180.8030.789
2020년 사고사망자수0.6350.7260.8070.8530.8090.8230.8350.8710.8600.8550.8740.8310.7970.8780.8740.8181.0000.8110.789
2021년 사고사망자수0.5080.6500.7520.7800.7290.7890.7650.8150.7950.7620.8080.7540.7390.7080.8280.8030.8111.0000.844
2022년 사고사망자수0.5680.6690.7250.7510.7060.7340.7050.8100.7310.7140.7340.7250.6900.7010.7950.7890.7890.8441.000

Missing values

2023-12-12T11:27:14.794973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:27:15.056148image/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

구분2004년 사고사망자수2005년 사고사망자수2006년 사고사망자수2007년 사고사망자수2008년 사고사망자수2009년 사고사망자수2010년 사고사망자수2011년 사고사망자수2012년 사고사망자수2013년 사고사망자수2014년 사고사망자수2015년 사고사망자수2016년 사고사망자수2017년 사고사망자수2018년 사고사망자수2019년 사고사망자수2020년 사고사망자수2021년 사고사망자수2022년 사고사망자수
0서 울 청3722182228243112311417141320137121511
1서울강남4535191116713291081764777515
2서울동부26332229202824241914131918211812151618
3서울서부21171814101113122192112131614414613
4서울남부23183225182123221425181617211614231213
5서울북부2621161313201721161216111416810345
6서울관악301918181915141513161114121571211810
7의 정 부79653830274937453438362838504030334047
8강 원1915121514101514111215121361812101415
9태 백947106847978791034775
구분2004년 사고사망자수2005년 사고사망자수2006년 사고사망자수2007년 사고사망자수2008년 사고사망자수2009년 사고사망자수2010년 사고사망자수2011년 사고사망자수2012년 사고사망자수2013년 사고사망자수2014년 사고사망자수2015년 사고사망자수2016년 사고사망자수2017년 사고사망자수2018년 사고사망자수2019년 사고사망자수2020년 사고사망자수2021년 사고사망자수2022년 사고사망자수
39군 산111214111919121316211410131588998
40목 포1615724282825162223181316181415221613
41여 수31182229232530282431251718181517172226
42제 주81771111151212107614181516106108
43대 전 청38364336522836443336303732294729322632
44청 주34223122263419363535313139332624202221
45충 주1213131512192225212222269222318261215
46천 안47392941364941413351412928363244282432
47보 령2516221710132713212321182320189151610
48서 산0000000000000005477