Overview

Dataset statistics

Number of variables19
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.2 KiB
Average record size in memory170.6 B

Variable types

Text3
Numeric16

Dataset

Description산재예방정책과에서 게시하는 <산업재해현황분석> 책자를 참고하여 2014년부터 2022년도까지의 사업장에서 발생한 산업재해 재해자수와 사망자수로 나누어 나열하였습니다.
Author고용노동부
URLhttps://www.data.go.kr/data/15002274/fileData.do

Alerts

2014년 재해자수 is highly overall correlated with 2014년 사망자수 and 12 other fieldsHigh correlation
2014년 사망자수 is highly overall correlated with 2014년 재해자수 and 14 other fieldsHigh correlation
2015년 재해자수 is highly overall correlated with 2014년 재해자수 and 12 other fieldsHigh correlation
2015년 사망자수 is highly overall correlated with 2014년 사망자수 and 11 other fieldsHigh correlation
2016년 재해자수 is highly overall correlated with 2014년 재해자수 and 12 other fieldsHigh correlation
2016년 사망자수 is highly overall correlated with 2014년 재해자수 and 14 other fieldsHigh correlation
2017년 재해자수 is highly overall correlated with 2014년 재해자수 and 13 other fieldsHigh correlation
2017년 사망자수 is highly overall correlated with 2014년 재해자수 and 14 other fieldsHigh correlation
2018년 재해자수 is highly overall correlated with 2014년 재해자수 and 14 other fieldsHigh correlation
2018년 사망자수 is highly overall correlated with 2014년 재해자수 and 14 other fieldsHigh correlation
2019년 재해자수 is highly overall correlated with 2014년 재해자수 and 13 other fieldsHigh correlation
2019년 사망자수 is highly overall correlated with 2014년 재해자수 and 14 other fieldsHigh correlation
2020년 재해자수 is highly overall correlated with 2014년 재해자수 and 14 other fieldsHigh correlation
2020년 사망자수 is highly overall correlated with 2014년 사망자수 and 9 other fieldsHigh correlation
2021년 사망자수 is highly overall correlated with 2014년 재해자수 and 14 other fieldsHigh correlation
2022년 사망자수 is highly overall correlated with 2014년 재해자수 and 14 other fieldsHigh correlation
구분 has unique valuesUnique
2015년 재해자수 has unique valuesUnique
2016년 재해자수 has unique valuesUnique
2017년 재해자수 has unique valuesUnique
2018년 재해자수 has unique valuesUnique
2020년 재해자수 has unique valuesUnique
2022년 재해자수 has unique valuesUnique
2014년 재해자수 has 1 (2.0%) zerosZeros
2014년 사망자수 has 1 (2.0%) zerosZeros
2015년 재해자수 has 1 (2.0%) zerosZeros
2015년 사망자수 has 1 (2.0%) zerosZeros
2016년 재해자수 has 1 (2.0%) zerosZeros
2016년 사망자수 has 1 (2.0%) zerosZeros
2017년 재해자수 has 1 (2.0%) zerosZeros
2017년 사망자수 has 1 (2.0%) zerosZeros
2018년 재해자수 has 1 (2.0%) zerosZeros
2018년 사망자수 has 1 (2.0%) zerosZeros

Reproduction

Analysis started2024-03-14 15:51:48.154263
Analysis finished2024-03-14 15:52:53.342926
Duration1 minute and 5.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size520.0 B
2024-03-15T00:52:54.000870image/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%
2024-03-15T00:52:55.297455image/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%

2014년 재해자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1855.2857
Minimum0
Maximum5289
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size569.0 B
2024-03-15T00:52:55.609190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile629
Q11161
median1777
Q32460
95-th percentile3348.2
Maximum5289
Range5289
Interquartile range (IQR)1299

Descriptive statistics

Standard deviation982.07484
Coefficient of variation (CV)0.52933887
Kurtosis1.6822979
Mean1855.2857
Median Absolute Deviation (MAD)677
Skewness0.85816155
Sum90909
Variance964471
MonotonicityNot monotonic
2024-03-15T00:52:55.991647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
869 2
 
4.1%
2379 1
 
2.0%
2135 1
 
2.0%
1217 1
 
2.0%
1333 1
 
2.0%
2460 1
 
2.0%
2607 1
 
2.0%
1793 1
 
2.0%
827 1
 
2.0%
653 1
 
2.0%
Other values (38) 38
77.6%
ValueCountFrequency (%)
0 1
2.0%
488 1
2.0%
613 1
2.0%
653 1
2.0%
682 1
2.0%
769 1
2.0%
827 1
2.0%
869 2
4.1%
892 1
2.0%
1039 1
2.0%
ValueCountFrequency (%)
5289 1
2.0%
3393 1
2.0%
3355 1
2.0%
3338 1
2.0%
3169 1
2.0%
3151 1
2.0%
2877 1
2.0%
2773 1
2.0%
2672 1
2.0%
2607 1
2.0%

2014년 사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.755102
Minimum0
Maximum185
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size569.0 B
2024-03-15T00:52:56.378814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.4
Q123
median30
Q346
95-th percentile65.4
Maximum185
Range185
Interquartile range (IQR)23

Descriptive statistics

Standard deviation27.848198
Coefficient of variation (CV)0.73760091
Kurtosis15.878554
Mean37.755102
Median Absolute Deviation (MAD)11
Skewness3.237396
Sum1850
Variance775.52211
MonotonicityNot monotonic
2024-03-15T00:52:56.758729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
30 3
 
6.1%
42 3
 
6.1%
24 3
 
6.1%
23 3
 
6.1%
14 2
 
4.1%
22 2
 
4.1%
19 2
 
4.1%
49 2
 
4.1%
28 2
 
4.1%
40 2
 
4.1%
Other values (24) 25
51.0%
ValueCountFrequency (%)
0 1
 
2.0%
7 1
 
2.0%
8 1
 
2.0%
9 1
 
2.0%
14 2
4.1%
19 2
4.1%
21 1
 
2.0%
22 2
4.1%
23 3
6.1%
24 3
6.1%
ValueCountFrequency (%)
185 1
2.0%
91 1
2.0%
67 1
2.0%
63 1
2.0%
61 1
2.0%
60 1
2.0%
59 1
2.0%
54 1
2.0%
52 1
2.0%
49 2
4.1%

2015년 재해자수
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1839.3673
Minimum0
Maximum5023
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size569.0 B
2024-03-15T00:52:57.008102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile604.2
Q11215
median1698
Q32480
95-th percentile3259.4
Maximum5023
Range5023
Interquartile range (IQR)1265

Descriptive statistics

Standard deviation958.01593
Coefficient of variation (CV)0.52083992
Kurtosis1.1659089
Mean1839.3673
Median Absolute Deviation (MAD)696
Skewness0.74028849
Sum90129
Variance917794.53
MonotonicityNot monotonic
2024-03-15T00:52:57.292521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2350 1
 
2.0%
2056 1
 
2.0%
2418 1
 
2.0%
1241 1
 
2.0%
1371 1
 
2.0%
2534 1
 
2.0%
2591 1
 
2.0%
1693 1
 
2.0%
800 1
 
2.0%
671 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0 1
2.0%
563 1
2.0%
597 1
2.0%
615 1
2.0%
671 1
2.0%
738 1
2.0%
773 1
2.0%
800 1
2.0%
872 1
2.0%
939 1
2.0%
ValueCountFrequency (%)
5023 1
2.0%
3591 1
2.0%
3279 1
2.0%
3230 1
2.0%
3214 1
2.0%
2949 1
2.0%
2782 1
2.0%
2744 1
2.0%
2591 1
2.0%
2534 1
2.0%

2015년 사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.938776
Minimum0
Maximum151
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size569.0 B
2024-03-15T00:52:57.817101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.4
Q121
median31
Q345
95-th percentile78.8
Maximum151
Range151
Interquartile range (IQR)24

Descriptive statistics

Standard deviation25.69404
Coefficient of variation (CV)0.6955845
Kurtosis7.6032625
Mean36.938776
Median Absolute Deviation (MAD)11
Skewness2.2770113
Sum1810
Variance660.18367
MonotonicityNot monotonic
2024-03-15T00:52:58.342539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
25 4
 
8.2%
31 3
 
6.1%
17 3
 
6.1%
20 3
 
6.1%
21 3
 
6.1%
39 2
 
4.1%
35 2
 
4.1%
45 2
 
4.1%
51 2
 
4.1%
27 2
 
4.1%
Other values (23) 23
46.9%
ValueCountFrequency (%)
0 1
 
2.0%
7 1
 
2.0%
12 1
 
2.0%
13 1
 
2.0%
14 1
 
2.0%
17 3
6.1%
20 3
6.1%
21 3
6.1%
22 1
 
2.0%
24 1
 
2.0%
ValueCountFrequency (%)
151 1
2.0%
102 1
2.0%
80 1
2.0%
77 1
2.0%
67 1
2.0%
62 1
2.0%
53 1
2.0%
52 1
2.0%
51 2
4.1%
47 1
2.0%

2016년 재해자수
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1850.1224
Minimum0
Maximum5072
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size569.0 B
2024-03-15T00:52:58.802350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile659.4
Q11203
median1723
Q32464
95-th percentile3400.8
Maximum5072
Range5072
Interquartile range (IQR)1261

Descriptive statistics

Standard deviation970.69968
Coefficient of variation (CV)0.5246678
Kurtosis1.1873818
Mean1850.1224
Median Absolute Deviation (MAD)724
Skewness0.78176803
Sum90656
Variance942257.86
MonotonicityNot monotonic
2024-03-15T00:52:59.213702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2406 1
 
2.0%
1822 1
 
2.0%
2626 1
 
2.0%
1187 1
 
2.0%
1266 1
 
2.0%
2635 1
 
2.0%
2634 1
 
2.0%
1723 1
 
2.0%
777 1
 
2.0%
675 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0 1
2.0%
547 1
2.0%
649 1
2.0%
675 1
2.0%
688 1
2.0%
747 1
2.0%
777 1
2.0%
791 1
2.0%
918 1
2.0%
924 1
2.0%
ValueCountFrequency (%)
5072 1
2.0%
3708 1
2.0%
3410 1
2.0%
3387 1
2.0%
3028 1
2.0%
2917 1
2.0%
2771 1
2.0%
2638 1
2.0%
2635 1
2.0%
2634 1
2.0%

2016년 사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.265306
Minimum0
Maximum155
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size569.0 B
2024-03-15T00:52:59.572902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.8
Q123
median29
Q348
95-th percentile68.6
Maximum155
Range155
Interquartile range (IQR)25

Descriptive statistics

Standard deviation24.557565
Coefficient of variation (CV)0.67716413
Kurtosis10.389794
Mean36.265306
Median Absolute Deviation (MAD)13
Skewness2.449758
Sum1777
Variance603.07398
MonotonicityNot monotonic
2024-03-15T00:53:00.026912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
26 4
 
8.2%
25 3
 
6.1%
50 3
 
6.1%
18 3
 
6.1%
29 2
 
4.1%
48 2
 
4.1%
12 2
 
4.1%
27 2
 
4.1%
44 2
 
4.1%
34 2
 
4.1%
Other values (24) 24
49.0%
ValueCountFrequency (%)
0 1
 
2.0%
4 1
 
2.0%
10 1
 
2.0%
12 2
4.1%
14 1
 
2.0%
15 1
 
2.0%
18 3
6.1%
19 1
 
2.0%
22 1
 
2.0%
23 1
 
2.0%
ValueCountFrequency (%)
155 1
 
2.0%
71 1
 
2.0%
69 1
 
2.0%
68 1
 
2.0%
67 1
 
2.0%
64 1
 
2.0%
57 1
 
2.0%
52 1
 
2.0%
50 3
6.1%
48 2
4.1%

2017년 재해자수
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1833.6327
Minimum0
Maximum5009
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size569.0 B
2024-03-15T00:53:00.644474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile706.4
Q11150
median1728
Q32363
95-th percentile3281.6
Maximum5009
Range5009
Interquartile range (IQR)1213

Descriptive statistics

Standard deviation943.89337
Coefficient of variation (CV)0.51476689
Kurtosis1.3115342
Mean1833.6327
Median Absolute Deviation (MAD)626
Skewness0.77839681
Sum89848
Variance890934.7
MonotonicityNot monotonic
2024-03-15T00:53:01.262433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2268 1
 
2.0%
1853 1
 
2.0%
2398 1
 
2.0%
1150 1
 
2.0%
1488 1
 
2.0%
2601 1
 
2.0%
2495 1
 
2.0%
1649 1
 
2.0%
737 1
 
2.0%
738 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0 1
2.0%
476 1
2.0%
692 1
2.0%
728 1
2.0%
737 1
2.0%
738 1
2.0%
759 1
2.0%
793 1
2.0%
975 1
2.0%
980 1
2.0%
ValueCountFrequency (%)
5009 1
2.0%
3457 1
2.0%
3286 1
2.0%
3275 1
2.0%
3214 1
2.0%
2976 1
2.0%
2865 1
2.0%
2768 1
2.0%
2601 1
2.0%
2495 1
2.0%

2017년 사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.938776
Minimum0
Maximum157
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size569.0 B
2024-03-15T00:53:01.658150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.4
Q122
median35
Q345
95-th percentile87.6
Maximum157
Range157
Interquartile range (IQR)23

Descriptive statistics

Standard deviation27.690558
Coefficient of variation (CV)0.69332516
Kurtosis6.2770754
Mean39.938776
Median Absolute Deviation (MAD)13
Skewness2.1080979
Sum1957
Variance766.76701
MonotonicityNot monotonic
2024-03-15T00:53:02.000710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
18 3
 
6.1%
41 3
 
6.1%
22 3
 
6.1%
29 2
 
4.1%
25 2
 
4.1%
37 2
 
4.1%
43 2
 
4.1%
24 2
 
4.1%
58 1
 
2.0%
21 1
 
2.0%
Other values (28) 28
57.1%
ValueCountFrequency (%)
0 1
 
2.0%
8 1
 
2.0%
11 1
 
2.0%
12 1
 
2.0%
17 1
 
2.0%
18 3
6.1%
20 1
 
2.0%
21 1
 
2.0%
22 3
6.1%
24 2
4.1%
ValueCountFrequency (%)
157 1
2.0%
113 1
2.0%
92 1
2.0%
81 1
2.0%
71 1
2.0%
70 1
2.0%
69 1
2.0%
58 1
2.0%
56 1
2.0%
54 1
2.0%

2018년 재해자수
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2087.8571
Minimum0
Maximum5735
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size569.0 B
2024-03-15T00:53:02.283675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile811
Q11360
median1950
Q32649
95-th percentile3912.2
Maximum5735
Range5735
Interquartile range (IQR)1289

Descriptive statistics

Standard deviation1092.0387
Coefficient of variation (CV)0.52304283
Kurtosis1.3399056
Mean2087.8571
Median Absolute Deviation (MAD)694
Skewness0.83127184
Sum102305
Variance1192548.5
MonotonicityNot monotonic
2024-03-15T00:53:02.760218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2607 1
 
2.0%
2188 1
 
2.0%
2726 1
 
2.0%
1360 1
 
2.0%
1376 1
 
2.0%
2834 1
 
2.0%
2775 1
 
2.0%
1900 1
 
2.0%
822 1
 
2.0%
832 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0 1
2.0%
534 1
2.0%
807 1
2.0%
817 1
2.0%
822 1
2.0%
824 1
2.0%
832 1
2.0%
855 1
2.0%
1074 1
2.0%
1241 1
2.0%
ValueCountFrequency (%)
5735 1
2.0%
4101 1
2.0%
3923 1
2.0%
3896 1
2.0%
3750 1
2.0%
3289 1
2.0%
3249 1
2.0%
3100 1
2.0%
2834 1
2.0%
2775 1
2.0%

2018년 사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.714286
Minimum0
Maximum157
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size569.0 B
2024-03-15T00:53:03.141970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.4
Q125
median37
Q354
95-th percentile86.6
Maximum157
Range157
Interquartile range (IQR)29

Descriptive statistics

Standard deviation30.229676
Coefficient of variation (CV)0.69152854
Kurtosis4.9398788
Mean43.714286
Median Absolute Deviation (MAD)14
Skewness1.9245052
Sum2142
Variance913.83333
MonotonicityNot monotonic
2024-03-15T00:53:03.617410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
54 3
 
6.1%
23 2
 
4.1%
38 2
 
4.1%
46 2
 
4.1%
26 2
 
4.1%
36 2
 
4.1%
37 2
 
4.1%
29 2
 
4.1%
68 2
 
4.1%
34 2
 
4.1%
Other values (27) 28
57.1%
ValueCountFrequency (%)
0 1
2.0%
9 1
2.0%
12 1
2.0%
13 1
2.0%
15 1
2.0%
16 1
2.0%
20 1
2.0%
21 1
2.0%
22 1
2.0%
23 2
4.1%
ValueCountFrequency (%)
157 1
 
2.0%
144 1
 
2.0%
87 1
 
2.0%
86 1
 
2.0%
84 1
 
2.0%
75 1
 
2.0%
72 1
 
2.0%
68 2
4.1%
62 1
 
2.0%
54 3
6.1%

2019년 재해자수
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2229.4286
Minimum468
Maximum6135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size569.0 B
2024-03-15T00:53:04.045804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum468
5-th percentile846.2
Q11460
median2080
Q32853
95-th percentile4119.2
Maximum6135
Range5667
Interquartile range (IQR)1393

Descriptive statistics

Standard deviation1143.6009
Coefficient of variation (CV)0.51295697
Kurtosis1.4914763
Mean2229.4286
Median Absolute Deviation (MAD)720
Skewness0.94585211
Sum109242
Variance1307823.1
MonotonicityNot monotonic
2024-03-15T00:53:04.471900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
2866 2
 
4.1%
2786 1
 
2.0%
928 1
 
2.0%
1460 1
 
2.0%
1566 1
 
2.0%
3020 1
 
2.0%
2853 1
 
2.0%
1950 1
 
2.0%
848 1
 
2.0%
915 1
 
2.0%
Other values (38) 38
77.6%
ValueCountFrequency (%)
468 1
2.0%
583 1
2.0%
845 1
2.0%
848 1
2.0%
874 1
2.0%
913 1
2.0%
915 1
2.0%
928 1
2.0%
1066 1
2.0%
1249 1
2.0%
ValueCountFrequency (%)
6135 1
2.0%
4378 1
2.0%
4142 1
2.0%
4085 1
2.0%
4056 1
2.0%
3468 1
2.0%
3446 1
2.0%
3180 1
2.0%
3020 1
2.0%
3019 1
2.0%

2019년 사망자수
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.22449
Minimum7
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size569.0 B
2024-03-15T00:53:04.896512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14
Q127
median36
Q349
95-th percentile83
Maximum144
Range137
Interquartile range (IQR)22

Descriptive statistics

Standard deviation25.062643
Coefficient of variation (CV)0.60795519
Kurtosis5.1906956
Mean41.22449
Median Absolute Deviation (MAD)11
Skewness1.8644759
Sum2020
Variance628.13605
MonotonicityNot monotonic
2024-03-15T00:53:05.312296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
31 3
 
6.1%
18 3
 
6.1%
36 3
 
6.1%
34 2
 
4.1%
70 2
 
4.1%
38 2
 
4.1%
19 2
 
4.1%
14 2
 
4.1%
46 2
 
4.1%
25 1
 
2.0%
Other values (27) 27
55.1%
ValueCountFrequency (%)
7 1
 
2.0%
9 1
 
2.0%
14 2
4.1%
18 3
6.1%
19 2
4.1%
20 1
 
2.0%
23 1
 
2.0%
25 1
 
2.0%
27 1
 
2.0%
28 1
 
2.0%
ValueCountFrequency (%)
144 1
2.0%
103 1
2.0%
87 1
2.0%
77 1
2.0%
72 1
2.0%
70 2
4.1%
62 1
2.0%
57 1
2.0%
55 1
2.0%
51 1
2.0%

2020년 재해자수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2211.8163
Minimum459
Maximum6097
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size569.0 B
2024-03-15T00:53:05.611023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum459
5-th percentile832.8
Q11442
median2036
Q32774
95-th percentile4238.6
Maximum6097
Range5638
Interquartile range (IQR)1332

Descriptive statistics

Standard deviation1148.5278
Coefficient of variation (CV)0.51926908
Kurtosis1.5243541
Mean2211.8163
Median Absolute Deviation (MAD)738
Skewness0.99369327
Sum108379
Variance1319116.2
MonotonicityNot monotonic
2024-03-15T00:53:06.112632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2553 1
 
2.0%
2216 1
 
2.0%
2754 1
 
2.0%
1442 1
 
2.0%
1783 1
 
2.0%
2856 1
 
2.0%
2833 1
 
2.0%
1923 1
 
2.0%
864 1
 
2.0%
930 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
459 1
2.0%
574 1
2.0%
812 1
2.0%
864 1
2.0%
872 1
2.0%
887 1
2.0%
895 1
2.0%
930 1
2.0%
1057 1
2.0%
1116 1
2.0%
ValueCountFrequency (%)
6097 1
2.0%
4457 1
2.0%
4275 1
2.0%
4184 1
2.0%
4119 1
2.0%
3434 1
2.0%
3319 1
2.0%
3216 1
2.0%
3176 1
2.0%
2856 1
2.0%

2020년 사망자수
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.081633
Minimum5
Maximum154
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size569.0 B
2024-03-15T00:53:06.562729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile11.4
Q126
median36
Q351
95-th percentile90.6
Maximum154
Range149
Interquartile range (IQR)25

Descriptive statistics

Standard deviation27.425533
Coefficient of variation (CV)0.65172217
Kurtosis4.7738983
Mean42.081633
Median Absolute Deviation (MAD)15
Skewness1.7395907
Sum2062
Variance752.15986
MonotonicityNot monotonic
2024-03-15T00:53:07.045464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
35 2
 
4.1%
51 2
 
4.1%
59 2
 
4.1%
48 2
 
4.1%
34 2
 
4.1%
14 2
 
4.1%
56 2
 
4.1%
27 2
 
4.1%
41 2
 
4.1%
18 2
 
4.1%
Other values (28) 29
59.2%
ValueCountFrequency (%)
5 1
2.0%
6 1
2.0%
11 1
2.0%
12 1
2.0%
14 2
4.1%
16 1
2.0%
18 2
4.1%
20 1
2.0%
21 1
2.0%
22 1
2.0%
ValueCountFrequency (%)
154 1
2.0%
98 1
2.0%
95 1
2.0%
84 1
2.0%
80 1
2.0%
76 1
2.0%
75 1
2.0%
59 2
4.1%
56 2
4.1%
52 1
2.0%
Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size520.0 B
2024-03-15T00:53:08.109733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.7959184
Min length3

Characters and Unicode

Total characters235
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)95.9%

Sample

1st row2,758
2nd row2,024
3rd row2,901
4th row2,105
5th row2,224
ValueCountFrequency (%)
638 2
 
4.1%
2,758 1
 
2.0%
1,019 1
 
2.0%
2,024 1
 
2.0%
3,853 1
 
2.0%
2,990 1
 
2.0%
1,498 1
 
2.0%
2,147 1
 
2.0%
3,321 1
 
2.0%
3,099 1
 
2.0%
Other values (38) 38
77.6%
2024-03-15T00:53:09.417115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 44
18.7%
1 32
13.6%
2 29
12.3%
3 20
8.5%
0 20
8.5%
8 19
8.1%
9 17
 
7.2%
7 15
 
6.4%
4 14
 
6.0%
6 13
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 191
81.3%
Other Punctuation 44
 
18.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 32
16.8%
2 29
15.2%
3 20
10.5%
0 20
10.5%
8 19
9.9%
9 17
8.9%
7 15
7.9%
4 14
7.3%
6 13
6.8%
5 12
 
6.3%
Other Punctuation
ValueCountFrequency (%)
, 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 235
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 44
18.7%
1 32
13.6%
2 29
12.3%
3 20
8.5%
0 20
8.5%
8 19
8.1%
9 17
 
7.2%
7 15
 
6.4%
4 14
 
6.0%
6 13
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 235
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 44
18.7%
1 32
13.6%
2 29
12.3%
3 20
8.5%
0 20
8.5%
8 19
8.1%
9 17
 
7.2%
7 15
 
6.4%
4 14
 
6.0%
6 13
 
5.5%

2021년 사망자수
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.44898
Minimum9
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size569.0 B
2024-03-15T00:53:09.808269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile13.2
Q128
median37
Q352
95-th percentile93.8
Maximum119
Range110
Interquartile range (IQR)24

Descriptive statistics

Standard deviation23.912219
Coefficient of variation (CV)0.5633167
Kurtosis2.4863527
Mean42.44898
Median Absolute Deviation (MAD)13
Skewness1.4232264
Sum2080
Variance571.79422
MonotonicityNot monotonic
2024-03-15T00:53:10.089397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
22 4
 
8.2%
38 3
 
6.1%
34 3
 
6.1%
37 3
 
6.1%
52 2
 
4.1%
47 2
 
4.1%
48 2
 
4.1%
29 2
 
4.1%
58 2
 
4.1%
44 1
 
2.0%
Other values (25) 25
51.0%
ValueCountFrequency (%)
9 1
 
2.0%
11 1
 
2.0%
12 1
 
2.0%
15 1
 
2.0%
16 1
 
2.0%
18 1
 
2.0%
22 4
8.2%
23 1
 
2.0%
24 1
 
2.0%
28 1
 
2.0%
ValueCountFrequency (%)
119 1
2.0%
112 1
2.0%
99 1
2.0%
86 1
2.0%
75 1
2.0%
62 1
2.0%
58 2
4.1%
57 1
2.0%
56 1
2.0%
55 1
2.0%
Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size520.0 B
2024-03-15T00:53:11.035446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.877551
Min length3

Characters and Unicode

Total characters239
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
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 row3,024
2nd row1,895
3rd row3,834
4th row2,287
5th row2,470
ValueCountFrequency (%)
3,024 1
 
2.0%
2,929 1
 
2.0%
3,038 1
 
2.0%
1,526 1
 
2.0%
2,054 1
 
2.0%
3,634 1
 
2.0%
3,225 1
 
2.0%
2,533 1
 
2.0%
1,046 1
 
2.0%
1,099 1
 
2.0%
Other values (39) 39
79.6%
2024-03-15T00:53:12.480351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 46
19.2%
3 32
13.4%
1 28
11.7%
2 26
10.9%
0 22
9.2%
5 19
7.9%
4 18
 
7.5%
9 13
 
5.4%
6 13
 
5.4%
8 11
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 193
80.8%
Other Punctuation 46
 
19.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 32
16.6%
1 28
14.5%
2 26
13.5%
0 22
11.4%
5 19
9.8%
4 18
9.3%
9 13
6.7%
6 13
6.7%
8 11
 
5.7%
7 11
 
5.7%
Other Punctuation
ValueCountFrequency (%)
, 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 46
19.2%
3 32
13.4%
1 28
11.7%
2 26
10.9%
0 22
9.2%
5 19
7.9%
4 18
 
7.5%
9 13
 
5.4%
6 13
 
5.4%
8 11
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 46
19.2%
3 32
13.4%
1 28
11.7%
2 26
10.9%
0 22
9.2%
5 19
7.9%
4 18
 
7.5%
9 13
 
5.4%
6 13
 
5.4%
8 11
 
4.6%

2022년 사망자수
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.367347
Minimum9
Maximum178
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size569.0 B
2024-03-15T00:53:13.126817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile12.4
Q123
median39
Q353
95-th percentile112.4
Maximum178
Range169
Interquartile range (IQR)30

Descriptive statistics

Standard deviation31.9144
Coefficient of variation (CV)0.7034663
Kurtosis6.0009901
Mean45.367347
Median Absolute Deviation (MAD)16
Skewness2.120423
Sum2223
Variance1018.5289
MonotonicityNot monotonic
2024-03-15T00:53:13.552618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
23 3
 
6.1%
56 2
 
4.1%
38 2
 
4.1%
46 2
 
4.1%
18 2
 
4.1%
39 2
 
4.1%
9 2
 
4.1%
65 1
 
2.0%
40 1
 
2.0%
28 1
 
2.0%
Other values (31) 31
63.3%
ValueCountFrequency (%)
9 2
4.1%
12 1
 
2.0%
13 1
 
2.0%
14 1
 
2.0%
17 1
 
2.0%
18 2
4.1%
20 1
 
2.0%
21 1
 
2.0%
23 3
6.1%
26 1
 
2.0%
ValueCountFrequency (%)
178 1
2.0%
126 1
2.0%
118 1
2.0%
104 1
2.0%
78 1
2.0%
73 1
2.0%
70 1
2.0%
65 1
2.0%
57 1
2.0%
56 2
4.1%

Interactions

2024-03-15T00:52:48.856305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:51:49.189379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:51:53.888261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:51:57.676208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:02.244451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:06.178941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:10.259959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:14.796891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-15T00:52:51.429695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:51:53.123909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:51:56.825709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:01.423900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:05.489683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:09.049661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:14.021217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:17.309957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:21.130915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:25.291083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:29.398906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:33.767973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:37.324031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:41.375979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:44.970886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:48.324054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:51.825475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:51:53.398654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:51:57.114395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:01.687540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:05.763183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:09.540065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:14.286474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:17.524857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:21.301680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:25.570174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:29.566628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:34.036989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:37.587712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:41.536197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:45.241708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:48.552912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:52.082627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:51:53.649913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:51:57.370606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:01.988587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:05.924036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:09.861520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:14.540732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:17.776612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:21.594670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:25.827224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:29.854599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:34.255703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:37.835791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:41.679690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:45.492123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:52:48.708141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T00:53:13.850892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분2014년 재해자수2014년 사망자수2015년 재해자수2015년 사망자수2016년 재해자수2016년 사망자수2017년 재해자수2017년 사망자수2018년 재해자수2018년 사망자수2019년 재해자수2019년 사망자수2020년 재해자수2020년 사망자수2021년 재해자수2021년 사망자수2022년 재해자수2022년 사망자수
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2014년 재해자수1.0001.0000.6040.9280.7970.9110.7040.9890.8160.9450.6980.9590.8930.8960.7741.0000.6741.0000.827
2014년 사망자수1.0000.6041.0000.5590.9250.4830.9590.5400.8920.6140.7690.6000.8820.6810.8591.0000.9121.0000.831
2015년 재해자수1.0000.9280.5591.0000.6170.9950.7090.9490.6230.9920.6720.9700.6440.9000.6630.0000.7891.0000.674
2015년 사망자수1.0000.7970.9250.6171.0000.6130.8300.8000.9670.6600.8360.7970.9470.6600.9371.0000.8831.0000.939
2016년 재해자수1.0000.9110.4830.9950.6131.0000.6650.9480.6540.9900.6580.9590.6070.8860.6470.0000.8071.0000.689
2016년 사망자수1.0000.7040.9590.7090.8300.6651.0000.6510.8540.7330.7360.6700.8020.7460.8241.0000.8261.0000.824
2017년 재해자수1.0000.9890.5400.9490.8000.9480.6511.0000.8170.9840.6340.9760.8430.9020.7481.0000.6931.0000.794
2017년 사망자수1.0000.8160.8920.6230.9670.6540.8540.8171.0000.6730.8060.7280.9600.5090.9381.0000.8581.0000.942
2018년 재해자수1.0000.9450.6140.9920.6600.9900.7330.9840.6731.0000.6910.9230.6890.9700.6661.0000.8181.0000.688
2018년 사망자수1.0000.6980.7690.6720.8360.6580.7360.6340.8060.6911.0000.6240.8180.6190.7901.0000.7921.0000.769
2019년 재해자수1.0000.9590.6000.9700.7970.9590.6700.9760.7280.9230.6241.0000.8490.9560.7711.0000.7351.0000.782
2019년 사망자수1.0000.8930.8820.6440.9470.6070.8020.8430.9600.6890.8180.8491.0000.7100.9251.0000.8261.0000.931
2020년 재해자수1.0000.8960.6810.9000.6600.8860.7460.9020.5090.9700.6190.9560.7101.0000.5921.0000.8531.0000.628
2020년 사망자수1.0000.7740.8590.6630.9370.6470.8240.7480.9380.6660.7900.7710.9250.5921.0001.0000.8451.0000.952
2021년 재해자수1.0001.0001.0000.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2021년 사망자수1.0000.6740.9120.7890.8830.8070.8260.6930.8580.8180.7920.7350.8260.8530.8451.0001.0001.0000.876
2022년 재해자수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2022년 사망자수1.0000.8270.8310.6740.9390.6890.8240.7940.9420.6880.7690.7820.9310.6280.9521.0000.8761.0001.000
2024-03-15T00:53:14.285855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2014년 재해자수2014년 사망자수2015년 재해자수2015년 사망자수2016년 재해자수2016년 사망자수2017년 재해자수2017년 사망자수2018년 재해자수2018년 사망자수2019년 재해자수2019년 사망자수2020년 재해자수2020년 사망자수2021년 사망자수2022년 사망자수
2014년 재해자수1.0000.5600.9950.4820.9900.5860.9890.5550.9820.5190.9810.5380.9790.4470.5350.500
2014년 사망자수0.5601.0000.5580.8990.5610.9000.5790.8360.5970.8780.5690.8030.6010.8660.8520.821
2015년 재해자수0.9950.5581.0000.4830.9940.5970.9910.5680.9850.5210.9810.5490.9790.4530.5490.508
2015년 사망자수0.4820.8990.4831.0000.4910.8960.5160.8730.5400.9370.5040.8390.5260.8610.8650.834
2016년 재해자수0.9900.5610.9940.4911.0000.6080.9920.5690.9880.5320.9840.5490.9790.4790.5590.519
2016년 사망자수0.5860.9000.5970.8960.6081.0000.6190.9180.6370.8990.6050.8390.6300.8690.8740.867
2017년 재해자수0.9890.5790.9910.5160.9920.6191.0000.5920.9910.5570.9870.5790.9870.4890.5730.538
2017년 사망자수0.5550.8360.5680.8730.5690.9180.5921.0000.6060.8840.5710.8400.6010.8200.8430.858
2018년 재해자수0.9820.5970.9850.5400.9880.6370.9910.6061.0000.5810.9870.6100.9820.5180.5980.564
2018년 사망자수0.5190.8780.5210.9370.5320.8990.5570.8840.5811.0000.5460.8660.5670.8810.8700.877
2019년 재해자수0.9810.5690.9810.5040.9840.6050.9870.5710.9870.5461.0000.5820.9900.4990.5580.540
2019년 사망자수0.5380.8030.5490.8390.5490.8390.5790.8400.6100.8660.5821.0000.5960.8210.8420.849
2020년 재해자수0.9790.6010.9790.5260.9790.6300.9870.6010.9820.5670.9900.5961.0000.5210.5880.562
2020년 사망자수0.4470.8660.4530.8610.4790.8690.4890.8200.5180.8810.4990.8210.5211.0000.8730.892
2021년 사망자수0.5350.8520.5490.8650.5590.8740.5730.8430.5980.8700.5580.8420.5880.8731.0000.906
2022년 사망자수0.5000.8210.5080.8340.5190.8670.5380.8580.5640.8770.5400.8490.5620.8920.9061.000

Missing values

2024-03-15T00:52:52.473288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T00:52:53.178499image/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

구분2014년 재해자수2014년 사망자수2015년 재해자수2015년 사망자수2016년 재해자수2016년 사망자수2017년 재해자수2017년 사망자수2018년 재해자수2018년 사망자수2019년 재해자수2019년 사망자수2020년 재해자수2020년 사망자수2021년 재해자수2021년 사망자수2022년 재해자수2022년 사망자수
0서 울 청2379362350312406462268582607422786342553422,758523,02456
1서울강남1448221337241320181263171459241570271561312,024361,89535
2서울동부1970241962312032262023392394462587402495322,901343,83456
3서울서부1577351594251504251587291803261988231848282,105282,28733
4서울남부1764271672271796331834322182362360332129522,224332,47049
5서울북부1531211569171565251592341857271856341734181,884232,10421
6서울관악1777271745201691221728252053151875362036222,086242,34123
7중 부 청2773402744442771342865373289683180553216413,576453,95938
8인천북부2454282514202464352340362644312866472799513,188473,46751
9부 천2197242341272350322245382522342710392626333,354483,03036
구분2014년 재해자수2014년 사망자수2015년 재해자수2015년 사망자수2016년 재해자수2016년 사망자수2017년 재해자수2017년 사망자수2018년 재해자수2018년 사망자수2019년 재해자수2019년 사망자수2020년 재해자수2020년 사망자수2021년 재해자수2021년 사망자수2022년 재해자수2022년 사망자수
39군 산892198721379115759188171387414887119521695812
40목 포1160221215171247231102241241231588251646371,875221,81423
41여 수1226301290251311261300241497301667281893441,938381,97239
42제 주120081160141207181297181315221360141207121,300181,43613
43대 전 청3151423214523410443286453896724378514457565,095575,36655
44청 주2224442233452269522175432528432733462750412,962383,22946
45천 안2672542480392457372472562732623019703176483,438473,65257
46충 주1311461233531203291258401408541631461565511,707311,75638
47보 령1161591317621400681245811410751066501057841,115751,13170
48서 산000000000046874596638115729