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 질병으로 사망하신 분들의 수에 대한 데이터입니다.
URLhttps://www.data.go.kr/data/15084671/fileData.do

Alerts

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

Reproduction

Analysis started2023-12-12 15:12:00.701722
Analysis finished2023-12-12 15:12:42.271247
Duration41.57 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
2023-12-13T00:12:42.440787image/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%
2023-12-13T00:12:42.831824image/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 질병사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.285714
Minimum0
Maximum156
Zeros3
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:12:43.007162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.8
Q112
median19
Q328
95-th percentile79.2
Maximum156
Range156
Interquartile range (IQR)16

Descriptive statistics

Standard deviation28.213472
Coefficient of variation (CV)1.0733386
Kurtosis9.4565847
Mean26.285714
Median Absolute Deviation (MAD)9
Skewness2.7375404
Sum1288
Variance796
MonotonicityNot monotonic
2023-12-13T00:12:43.142142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 3
 
6.1%
14 3
 
6.1%
24 3
 
6.1%
5 2
 
4.1%
28 2
 
4.1%
12 2
 
4.1%
10 2
 
4.1%
41 2
 
4.1%
4 2
 
4.1%
13 2
 
4.1%
Other values (24) 26
53.1%
ValueCountFrequency (%)
0 3
6.1%
2 1
 
2.0%
4 2
4.1%
5 2
4.1%
6 1
 
2.0%
7 1
 
2.0%
10 2
4.1%
12 2
4.1%
13 2
4.1%
14 3
6.1%
ValueCountFrequency (%)
156 1
2.0%
100 1
2.0%
94 1
2.0%
57 1
2.0%
56 1
2.0%
55 1
2.0%
42 1
2.0%
41 2
4.1%
39 1
2.0%
38 1
2.0%

2005년 질병사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.346939
Minimum0
Maximum161
Zeros3
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:12:43.310776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.8
Q17
median16
Q326
95-th percentile60.8
Maximum161
Range161
Interquartile range (IQR)19

Descriptive statistics

Standard deviation26.43589
Coefficient of variation (CV)1.1829759
Kurtosis15.520881
Mean22.346939
Median Absolute Deviation (MAD)9
Skewness3.409737
Sum1095
Variance698.85629
MonotonicityNot monotonic
2023-12-13T00:12:43.471576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4 4
 
8.2%
11 4
 
8.2%
0 3
 
6.1%
13 3
 
6.1%
22 3
 
6.1%
16 3
 
6.1%
31 2
 
4.1%
5 2
 
4.1%
15 2
 
4.1%
7 2
 
4.1%
Other values (19) 21
42.9%
ValueCountFrequency (%)
0 3
6.1%
2 1
 
2.0%
3 1
 
2.0%
4 4
8.2%
5 2
4.1%
7 2
4.1%
10 2
4.1%
11 4
8.2%
13 3
6.1%
15 2
4.1%
ValueCountFrequency (%)
161 1
2.0%
79 1
2.0%
62 1
2.0%
59 1
2.0%
49 1
2.0%
48 1
2.0%
41 1
2.0%
36 1
2.0%
31 2
4.1%
29 1
2.0%

2006년 질병사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.877551
Minimum0
Maximum163
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:12:43.643636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.4
Q18
median15
Q324
95-th percentile61.8
Maximum163
Range163
Interquartile range (IQR)16

Descriptive statistics

Standard deviation28.56107
Coefficient of variation (CV)1.2484321
Kurtosis14.158084
Mean22.877551
Median Absolute Deviation (MAD)8
Skewness3.5295265
Sum1121
Variance815.73469
MonotonicityNot monotonic
2023-12-13T00:12:43.832016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
14 4
 
8.2%
7 4
 
8.2%
17 3
 
6.1%
6 3
 
6.1%
22 3
 
6.1%
15 3
 
6.1%
5 2
 
4.1%
24 2
 
4.1%
25 2
 
4.1%
29 2
 
4.1%
Other values (19) 21
42.9%
ValueCountFrequency (%)
0 1
 
2.0%
2 1
 
2.0%
4 1
 
2.0%
5 2
4.1%
6 3
6.1%
7 4
8.2%
8 1
 
2.0%
9 1
 
2.0%
10 2
4.1%
12 1
 
2.0%
ValueCountFrequency (%)
163 1
2.0%
122 1
2.0%
67 1
2.0%
54 1
2.0%
53 1
2.0%
39 1
2.0%
30 1
2.0%
29 2
4.1%
25 2
4.1%
24 2
4.1%

2007년 질병사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.877551
Minimum0
Maximum134
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:12:43.983159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.8
Q110
median14
Q322
95-th percentile60
Maximum134
Range134
Interquartile range (IQR)12

Descriptive statistics

Standard deviation24.821557
Coefficient of variation (CV)1.1889113
Kurtosis12.527682
Mean20.877551
Median Absolute Deviation (MAD)6
Skewness3.3892248
Sum1023
Variance616.10969
MonotonicityNot monotonic
2023-12-13T00:12:44.136636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20 4
 
8.2%
13 4
 
8.2%
12 4
 
8.2%
22 3
 
6.1%
10 3
 
6.1%
7 2
 
4.1%
19 2
 
4.1%
6 2
 
4.1%
8 2
 
4.1%
15 2
 
4.1%
Other values (20) 21
42.9%
ValueCountFrequency (%)
0 1
 
2.0%
1 1
 
2.0%
2 1
 
2.0%
4 1
 
2.0%
5 1
 
2.0%
6 2
4.1%
7 2
4.1%
8 2
4.1%
9 1
 
2.0%
10 3
6.1%
ValueCountFrequency (%)
134 1
2.0%
116 1
2.0%
70 1
2.0%
45 1
2.0%
35 1
2.0%
34 1
2.0%
31 1
2.0%
26 1
2.0%
25 1
2.0%
23 1
2.0%

2008년 질병사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.877551
Minimum0
Maximum118
Zeros2
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:12:44.293811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.8
Q17
median14
Q322
95-th percentile62.4
Maximum118
Range118
Interquartile range (IQR)15

Descriptive statistics

Standard deviation23.17293
Coefficient of variation (CV)1.165784
Kurtosis9.3485139
Mean19.877551
Median Absolute Deviation (MAD)7
Skewness2.8910764
Sum974
Variance536.98469
MonotonicityNot monotonic
2023-12-13T00:12:44.429773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
12 4
 
8.2%
3 3
 
6.1%
4 3
 
6.1%
14 3
 
6.1%
28 3
 
6.1%
11 3
 
6.1%
8 3
 
6.1%
6 2
 
4.1%
21 2
 
4.1%
0 2
 
4.1%
Other values (17) 21
42.9%
ValueCountFrequency (%)
0 2
4.1%
1 1
 
2.0%
3 3
6.1%
4 3
6.1%
6 2
4.1%
7 2
4.1%
8 3
6.1%
11 3
6.1%
12 4
8.2%
13 1
 
2.0%
ValueCountFrequency (%)
118 1
 
2.0%
105 1
 
2.0%
68 1
 
2.0%
54 1
 
2.0%
40 1
 
2.0%
33 1
 
2.0%
28 3
6.1%
25 2
4.1%
23 1
 
2.0%
22 1
 
2.0%

2009년 질병사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.918367
Minimum0
Maximum119
Zeros3
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:12:44.552274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q17
median10
Q315
95-th percentile66.2
Maximum119
Range119
Interquartile range (IQR)8

Descriptive statistics

Standard deviation21.881153
Coefficient of variation (CV)1.3745853
Kurtosis11.578281
Mean15.918367
Median Absolute Deviation (MAD)5
Skewness3.2654205
Sum780
Variance478.78486
MonotonicityNot monotonic
2023-12-13T00:12:44.681500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
8 5
 
10.2%
7 5
 
10.2%
15 4
 
8.2%
10 4
 
8.2%
0 3
 
6.1%
12 3
 
6.1%
4 2
 
4.1%
9 2
 
4.1%
5 2
 
4.1%
18 2
 
4.1%
Other values (14) 17
34.7%
ValueCountFrequency (%)
0 3
6.1%
1 2
 
4.1%
3 1
 
2.0%
4 2
 
4.1%
5 2
 
4.1%
6 2
 
4.1%
7 5
10.2%
8 5
10.2%
9 2
 
4.1%
10 4
8.2%
ValueCountFrequency (%)
119 1
2.0%
80 1
2.0%
73 1
2.0%
56 1
2.0%
27 1
2.0%
26 1
2.0%
23 1
2.0%
18 2
4.1%
17 1
2.0%
16 2
4.1%

2010년 질병사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.673469
Minimum0
Maximum117
Zeros2
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:12:44.815677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.4
Q17
median11
Q318
95-th percentile55.6
Maximum117
Range117
Interquartile range (IQR)11

Descriptive statistics

Standard deviation21.509095
Coefficient of variation (CV)1.2900191
Kurtosis11.570015
Mean16.673469
Median Absolute Deviation (MAD)6
Skewness3.2158015
Sum817
Variance462.64116
MonotonicityNot monotonic
2023-12-13T00:12:44.936811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
7 5
 
10.2%
12 4
 
8.2%
8 4
 
8.2%
13 4
 
8.2%
5 4
 
8.2%
4 3
 
6.1%
11 2
 
4.1%
14 2
 
4.1%
18 2
 
4.1%
20 2
 
4.1%
Other values (15) 17
34.7%
ValueCountFrequency (%)
0 2
 
4.1%
1 1
 
2.0%
2 1
 
2.0%
3 1
 
2.0%
4 3
6.1%
5 4
8.2%
7 5
10.2%
8 4
8.2%
10 2
 
4.1%
11 2
 
4.1%
ValueCountFrequency (%)
117 1
2.0%
89 1
2.0%
56 1
2.0%
55 1
2.0%
35 1
2.0%
27 1
2.0%
24 1
2.0%
23 1
2.0%
21 1
2.0%
20 2
4.1%

2011년 질병사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.918367
Minimum0
Maximum116
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:12:45.054140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.4
Q15
median9
Q315
95-th percentile52.4
Maximum116
Range116
Interquartile range (IQR)10

Descriptive statistics

Standard deviation21.156005
Coefficient of variation (CV)1.418118
Kurtosis12.97489
Mean14.918367
Median Absolute Deviation (MAD)5
Skewness3.4412436
Sum731
Variance447.57653
MonotonicityNot monotonic
2023-12-13T00:12:45.191326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
8 6
 
12.2%
9 4
 
8.2%
4 4
 
8.2%
2 4
 
8.2%
16 3
 
6.1%
14 3
 
6.1%
12 3
 
6.1%
1 2
 
4.1%
19 2
 
4.1%
11 2
 
4.1%
Other values (14) 16
32.7%
ValueCountFrequency (%)
0 1
 
2.0%
1 2
 
4.1%
2 4
8.2%
3 1
 
2.0%
4 4
8.2%
5 2
 
4.1%
6 1
 
2.0%
7 2
 
4.1%
8 6
12.2%
9 4
8.2%
ValueCountFrequency (%)
116 1
 
2.0%
88 1
 
2.0%
54 1
 
2.0%
50 1
 
2.0%
32 1
 
2.0%
20 1
 
2.0%
19 2
4.1%
17 1
 
2.0%
16 3
6.1%
15 1
 
2.0%

2012년 질병사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.897959
Minimum0
Maximum91
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:12:45.335622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median11
Q315
95-th percentile43
Maximum91
Range91
Interquartile range (IQR)9

Descriptive statistics

Standard deviation17.935353
Coefficient of variation (CV)1.2038798
Kurtosis11.889866
Mean14.897959
Median Absolute Deviation (MAD)5
Skewness3.3242803
Sum730
Variance321.67687
MonotonicityNot monotonic
2023-12-13T00:12:45.452290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11 5
 
10.2%
3 4
 
8.2%
13 4
 
8.2%
7 4
 
8.2%
10 3
 
6.1%
2 3
 
6.1%
6 3
 
6.1%
15 3
 
6.1%
12 2
 
4.1%
21 2
 
4.1%
Other values (15) 16
32.7%
ValueCountFrequency (%)
0 1
 
2.0%
2 3
6.1%
3 4
8.2%
4 1
 
2.0%
5 1
 
2.0%
6 3
6.1%
7 4
8.2%
8 2
 
4.1%
10 3
6.1%
11 5
10.2%
ValueCountFrequency (%)
91 1
2.0%
89 1
2.0%
53 1
2.0%
28 1
2.0%
25 1
2.0%
22 1
2.0%
21 2
4.1%
20 1
2.0%
18 1
2.0%
17 1
2.0%

2013년 질병사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.122449
Minimum0
Maximum117
Zeros2
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:12:45.580665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.4
Q17
median11
Q318
95-th percentile59.6
Maximum117
Range117
Interquartile range (IQR)11

Descriptive statistics

Standard deviation21.391814
Coefficient of variation (CV)1.2493431
Kurtosis11.084573
Mean17.122449
Median Absolute Deviation (MAD)6
Skewness3.1439375
Sum839
Variance457.60969
MonotonicityNot monotonic
2023-12-13T00:12:45.717366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
5 4
 
8.2%
9 4
 
8.2%
12 3
 
6.1%
7 3
 
6.1%
18 3
 
6.1%
10 3
 
6.1%
2 2
 
4.1%
17 2
 
4.1%
11 2
 
4.1%
13 2
 
4.1%
Other values (15) 21
42.9%
ValueCountFrequency (%)
0 2
4.1%
1 1
 
2.0%
2 2
4.1%
4 1
 
2.0%
5 4
8.2%
6 2
4.1%
7 3
6.1%
8 1
 
2.0%
9 4
8.2%
10 3
6.1%
ValueCountFrequency (%)
117 1
 
2.0%
82 1
 
2.0%
70 1
 
2.0%
44 2
4.1%
30 1
 
2.0%
22 1
 
2.0%
20 2
4.1%
19 2
4.1%
18 3
6.1%
17 2
4.1%

2014년 질병사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.510204
Minimum0
Maximum177
Zeros2
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:12:45.856659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median12
Q319
95-th percentile36.8
Maximum177
Range177
Interquartile range (IQR)13

Descriptive statistics

Standard deviation27.067141
Coefficient of variation (CV)1.5457924
Kurtosis26.452953
Mean17.510204
Median Absolute Deviation (MAD)7
Skewness4.8099703
Sum858
Variance732.6301
MonotonicityNot monotonic
2023-12-13T00:12:45.986076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4 4
 
8.2%
20 4
 
8.2%
5 3
 
6.1%
14 3
 
6.1%
0 2
 
4.1%
13 2
 
4.1%
6 2
 
4.1%
7 2
 
4.1%
15 2
 
4.1%
8 2
 
4.1%
Other values (17) 23
46.9%
ValueCountFrequency (%)
0 2
4.1%
2 2
4.1%
3 1
 
2.0%
4 4
8.2%
5 3
6.1%
6 2
4.1%
7 2
4.1%
8 2
4.1%
9 2
4.1%
10 1
 
2.0%
ValueCountFrequency (%)
177 1
 
2.0%
89 1
 
2.0%
38 1
 
2.0%
35 1
 
2.0%
27 2
4.1%
24 1
 
2.0%
23 1
 
2.0%
20 4
8.2%
19 1
 
2.0%
18 1
 
2.0%

2015년 질병사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.44898
Minimum0
Maximum144
Zeros2
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:12:46.102160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.4
Q17
median11
Q315
95-th percentile56
Maximum144
Range144
Interquartile range (IQR)8

Descriptive statistics

Standard deviation24.693168
Coefficient of variation (CV)1.415164
Kurtosis16.019026
Mean17.44898
Median Absolute Deviation (MAD)4
Skewness3.7878291
Sum855
Variance609.75255
MonotonicityNot monotonic
2023-12-13T00:12:46.245097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11 5
 
10.2%
8 4
 
8.2%
12 4
 
8.2%
14 3
 
6.1%
6 3
 
6.1%
7 3
 
6.1%
15 3
 
6.1%
0 2
 
4.1%
10 2
 
4.1%
9 2
 
4.1%
Other values (15) 18
36.7%
ValueCountFrequency (%)
0 2
4.1%
2 1
 
2.0%
3 1
 
2.0%
4 2
4.1%
5 1
 
2.0%
6 3
6.1%
7 3
6.1%
8 4
8.2%
9 2
4.1%
10 2
4.1%
ValueCountFrequency (%)
144 1
2.0%
97 1
2.0%
64 1
2.0%
44 1
2.0%
42 1
2.0%
27 1
2.0%
23 1
2.0%
19 1
2.0%
18 1
2.0%
17 2
4.1%

2016년 질병사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.489796
Minimum0
Maximum146
Zeros2
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:12:46.369569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.4
Q17
median11
Q315
95-th percentile46.2
Maximum146
Range146
Interquartile range (IQR)8

Descriptive statistics

Standard deviation22.66157
Coefficient of variation (CV)1.3742784
Kurtosis22.553367
Mean16.489796
Median Absolute Deviation (MAD)4
Skewness4.2854889
Sum808
Variance513.54677
MonotonicityNot monotonic
2023-12-13T00:12:46.499278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
8 5
 
10.2%
11 4
 
8.2%
5 4
 
8.2%
12 3
 
6.1%
15 3
 
6.1%
33 2
 
4.1%
20 2
 
4.1%
2 2
 
4.1%
14 2
 
4.1%
13 2
 
4.1%
Other values (15) 20
40.8%
ValueCountFrequency (%)
0 2
 
4.1%
1 1
 
2.0%
2 2
 
4.1%
4 1
 
2.0%
5 4
8.2%
6 2
 
4.1%
7 2
 
4.1%
8 5
10.2%
9 2
 
4.1%
10 2
 
4.1%
ValueCountFrequency (%)
146 1
2.0%
61 1
2.0%
47 1
2.0%
45 1
2.0%
39 1
2.0%
33 2
4.1%
20 2
4.1%
19 1
2.0%
17 1
2.0%
16 1
2.0%

2017년 질병사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.265306
Minimum0
Maximum147
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:12:46.640635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18
median13
Q319
95-th percentile62.8
Maximum147
Range147
Interquartile range (IQR)11

Descriptive statistics

Standard deviation26.370577
Coefficient of variation (CV)1.3012671
Kurtosis12.811557
Mean20.265306
Median Absolute Deviation (MAD)6
Skewness3.3682687
Sum993
Variance695.40731
MonotonicityNot monotonic
2023-12-13T00:12:46.799201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
10 6
 
12.2%
3 4
 
8.2%
6 4
 
8.2%
18 4
 
8.2%
13 3
 
6.1%
16 3
 
6.1%
14 2
 
4.1%
4 2
 
4.1%
19 2
 
4.1%
8 2
 
4.1%
Other values (15) 17
34.7%
ValueCountFrequency (%)
0 1
 
2.0%
3 4
8.2%
4 2
 
4.1%
5 1
 
2.0%
6 4
8.2%
8 2
 
4.1%
10 6
12.2%
11 1
 
2.0%
12 1
 
2.0%
13 3
6.1%
ValueCountFrequency (%)
147 1
2.0%
109 1
2.0%
64 1
2.0%
61 1
2.0%
42 1
2.0%
41 1
2.0%
38 1
2.0%
23 2
4.1%
22 1
2.0%
20 1
2.0%

2018년 질병사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.897959
Minimum0
Maximum154
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:12:46.932571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q110
median16
Q325
95-th percentile69
Maximum154
Range154
Interquartile range (IQR)15

Descriptive statistics

Standard deviation28.801219
Coefficient of variation (CV)1.2051748
Kurtosis12.238947
Mean23.897959
Median Absolute Deviation (MAD)7
Skewness3.3430294
Sum1171
Variance829.5102
MonotonicityNot monotonic
2023-12-13T00:12:47.068843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
10 5
 
10.2%
5 4
 
8.2%
25 3
 
6.1%
16 3
 
6.1%
17 3
 
6.1%
19 3
 
6.1%
11 2
 
4.1%
8 2
 
4.1%
15 2
 
4.1%
14 1
 
2.0%
Other values (21) 21
42.9%
ValueCountFrequency (%)
0 1
 
2.0%
2 1
 
2.0%
5 4
8.2%
6 1
 
2.0%
8 2
 
4.1%
9 1
 
2.0%
10 5
10.2%
11 2
 
4.1%
12 1
 
2.0%
13 1
 
2.0%
ValueCountFrequency (%)
154 1
2.0%
135 1
2.0%
77 1
2.0%
57 1
2.0%
44 1
2.0%
36 1
2.0%
33 1
2.0%
32 1
2.0%
31 1
2.0%
30 1
2.0%

2019년 질병사망자수
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.77551
Minimum1
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:12:47.604393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q111
median19
Q327
95-th percentile58.4
Maximum140
Range139
Interquartile range (IQR)16

Descriptive statistics

Standard deviation24.008041
Coefficient of variation (CV)1.0097803
Kurtosis12.742963
Mean23.77551
Median Absolute Deviation (MAD)8
Skewness3.2460955
Sum1165
Variance576.38605
MonotonicityNot monotonic
2023-12-13T00:12:47.769101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
13 4
 
8.2%
20 3
 
6.1%
28 3
 
6.1%
6 3
 
6.1%
22 3
 
6.1%
11 2
 
4.1%
19 2
 
4.1%
24 2
 
4.1%
4 2
 
4.1%
41 2
 
4.1%
Other values (20) 23
46.9%
ValueCountFrequency (%)
1 1
 
2.0%
2 1
 
2.0%
4 2
4.1%
6 3
6.1%
8 1
 
2.0%
9 1
 
2.0%
10 2
4.1%
11 2
4.1%
13 4
8.2%
14 1
 
2.0%
ValueCountFrequency (%)
140 1
 
2.0%
101 1
 
2.0%
70 1
 
2.0%
41 2
4.1%
40 1
 
2.0%
38 1
 
2.0%
32 1
 
2.0%
29 1
 
2.0%
28 3
6.1%
27 1
 
2.0%

2020년 질병사망자수
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.081633
Minimum2
Maximum147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:12:47.886586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q113
median17
Q329
95-th percentile62.2
Maximum147
Range145
Interquartile range (IQR)16

Descriptive statistics

Standard deviation24.436002
Coefficient of variation (CV)1.0147153
Kurtosis13.551456
Mean24.081633
Median Absolute Deviation (MAD)8
Skewness3.2510324
Sum1180
Variance597.1182
MonotonicityNot monotonic
2023-12-13T00:12:48.002611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
13 5
 
10.2%
15 4
 
8.2%
2 4
 
8.2%
30 3
 
6.1%
17 3
 
6.1%
14 2
 
4.1%
29 2
 
4.1%
24 2
 
4.1%
10 2
 
4.1%
9 1
 
2.0%
Other values (21) 21
42.9%
ValueCountFrequency (%)
2 4
8.2%
4 1
 
2.0%
6 1
 
2.0%
8 1
 
2.0%
9 1
 
2.0%
10 2
 
4.1%
11 1
 
2.0%
13 5
10.2%
14 2
 
4.1%
15 4
8.2%
ValueCountFrequency (%)
147 1
 
2.0%
89 1
 
2.0%
69 1
 
2.0%
52 1
 
2.0%
51 1
 
2.0%
42 1
 
2.0%
33 1
 
2.0%
31 1
 
2.0%
30 3
6.1%
29 2
4.1%

2021년 질병사망자수
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.55102
Minimum1
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:12:48.117054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.4
Q116
median21
Q331
95-th percentile62
Maximum105
Range104
Interquartile range (IQR)15

Descriptive statistics

Standard deviation20.108105
Coefficient of variation (CV)0.78697855
Kurtosis6.310052
Mean25.55102
Median Absolute Deviation (MAD)8
Skewness2.2582974
Sum1252
Variance404.33588
MonotonicityNot monotonic
2023-12-13T00:12:48.230186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
19 5
 
10.2%
16 3
 
6.1%
10 3
 
6.1%
33 2
 
4.1%
17 2
 
4.1%
18 2
 
4.1%
21 2
 
4.1%
46 2
 
4.1%
8 2
 
4.1%
23 2
 
4.1%
Other values (21) 24
49.0%
ValueCountFrequency (%)
1 1
 
2.0%
4 1
 
2.0%
5 1
 
2.0%
6 1
 
2.0%
7 1
 
2.0%
8 2
4.1%
10 3
6.1%
14 1
 
2.0%
15 1
 
2.0%
16 3
6.1%
ValueCountFrequency (%)
105 1
2.0%
93 1
2.0%
64 1
2.0%
59 1
2.0%
46 2
4.1%
37 1
2.0%
36 1
2.0%
33 2
4.1%
32 1
2.0%
31 2
4.1%

2022년 질병사망자수
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.530612
Minimum2
Maximum173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:12:48.339829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q113
median20
Q334
95-th percentile58.8
Maximum173
Range171
Interquartile range (IQR)21

Descriptive statistics

Standard deviation29.446351
Coefficient of variation (CV)1.0695858
Kurtosis13.650171
Mean27.530612
Median Absolute Deviation (MAD)7
Skewness3.3380043
Sum1349
Variance867.08759
MonotonicityNot monotonic
2023-12-13T00:12:48.459568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20 4
 
8.2%
22 3
 
6.1%
25 3
 
6.1%
16 3
 
6.1%
13 3
 
6.1%
23 3
 
6.1%
9 2
 
4.1%
4 2
 
4.1%
21 2
 
4.1%
14 2
 
4.1%
Other values (21) 22
44.9%
ValueCountFrequency (%)
2 1
 
2.0%
3 1
 
2.0%
4 2
4.1%
5 1
 
2.0%
6 1
 
2.0%
7 1
 
2.0%
9 2
4.1%
10 1
 
2.0%
12 1
 
2.0%
13 3
6.1%
ValueCountFrequency (%)
173 1
2.0%
123 1
2.0%
60 1
2.0%
57 1
2.0%
52 1
2.0%
51 1
2.0%
50 1
2.0%
45 1
2.0%
39 1
2.0%
38 1
2.0%

Interactions

2023-12-13T00:12:39.894420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:01.329919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-13T00:12:14.577674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-13T00:12:13.606215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:15.551493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:17.676405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:19.898775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:22.044673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:24.157856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:26.160637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:28.603599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:31.031216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:33.015888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:34.860653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:37.590222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:39.222658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:40.692863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:02.967112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:05.702562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:07.835795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:09.709085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:11.882117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:13.693315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:15.637902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:17.769947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:20.062056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:22.145843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:24.225969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:26.293087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:28.733936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:31.159043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:33.112867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:34.958220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:37.705056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:39.295727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:40.766239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:03.198011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:05.816289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:07.949482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:09.808684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:11.984006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:13.801616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:15.746717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:17.910708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:20.230842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:22.255077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:24.298733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:26.441540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:28.875241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:31.267702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:33.208158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:35.066730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:37.803852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:39.378461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:40.842113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:03.406954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:05.911859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:08.079955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:10.223734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:12.073423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:13.902543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:15.834318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:18.047452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:20.422653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:22.361166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:24.372839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:26.573518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:29.008215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:31.385812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:33.320078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:35.176723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:37.904199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:39.450942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:40.933405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:03.525498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:06.007065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:08.193717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:10.325252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:12.159399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:14.020409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:16.261940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:18.178728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:20.538871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:22.444784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:24.452138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:26.710588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:29.119914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:31.491963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:33.423698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:35.303061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:38.015380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:39.525273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:41.028650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:03.659744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:06.105123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:08.298058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:10.425176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:12.252546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:14.128086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:16.364619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:18.301702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:20.637019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:22.568488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:24.533361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:26.848779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:29.224414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:31.583729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:33.535525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:35.752626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:38.130972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:39.601985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:41.390171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:04.130251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:06.197458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:08.389192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:10.535464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:12.335676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:14.226795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:16.460990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:18.414928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:20.730702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:22.659728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:24.617801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:26.980428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:29.327063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:31.676664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:33.652181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:35.865597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:38.217935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:39.680296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:41.477472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:04.250901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:06.296869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:08.477029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:10.617735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:12.419550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:14.358046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:16.553908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:18.509780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:20.839367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:23.112532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:24.701656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:27.097367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:29.753440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:31.790522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:33.769618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:35.987189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:38.317943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:39.749832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:41.573597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:04.387357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:06.409066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:08.549381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:10.689665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:12.494068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:14.469942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:16.637306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:18.594745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:20.941714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:23.222919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:24.791239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:27.220046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:29.834688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:31.888400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:33.859010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:36.100051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:38.400020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:39.825089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:12:48.560930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분2004년 질병사망자수2005년 질병사망자수2006년 질병사망자수2007년 질병사망자수2008년 질병사망자수2009년 질병사망자수2010년 질병사망자수2011년 질병사망자수2012년 질병사망자수2013년 질병사망자수2014년 질병사망자수2015년 질병사망자수2016년 질병사망자수2017년 질병사망자수2018년 질병사망자수2019년 질병사망자수2020년 질병사망자수2021년 질병사망자수2022년 질병사망자수
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2004년 질병사망자수1.0001.0000.9550.8430.8300.8570.9290.9250.9230.8600.7880.7940.8050.9320.9400.8340.9350.7870.9240.943
2005년 질병사망자수1.0000.9551.0000.8840.8970.8990.9380.9680.9740.8990.8530.8570.8730.9750.9810.8690.9580.8470.9090.958
2006년 질병사망자수1.0000.8430.8841.0000.9810.9340.8830.8810.9110.8080.9870.9330.9850.8690.8700.9790.8570.9880.8830.876
2007년 질병사망자수1.0000.8300.8970.9811.0000.9490.9190.8950.9100.8680.9800.9420.9930.9170.9220.9930.9420.9830.9160.876
2008년 질병사망자수1.0000.8570.8990.9340.9491.0000.9520.9460.9690.9130.9360.9410.9740.9250.9280.9480.9180.9360.9070.900
2009년 질병사망자수1.0000.9290.9380.8830.9190.9521.0000.9590.9700.9270.9290.8940.9200.9710.9740.9030.9500.8960.9440.955
2010년 질병사망자수1.0000.9250.9680.8810.8950.9460.9591.0000.9890.9230.9020.9000.9140.9830.9790.9220.9650.9040.9480.966
2011년 질병사망자수1.0000.9230.9740.9110.9100.9690.9700.9891.0000.9670.9150.9100.9540.9850.9840.9130.9670.9140.9330.957
2012년 질병사망자수1.0000.8600.8990.8080.8680.9130.9270.9230.9671.0000.8180.7250.9040.9350.9370.8390.9650.8000.8930.911
2013년 질병사망자수1.0000.7880.8530.9870.9800.9360.9290.9020.9150.8181.0000.9410.9850.9040.8890.9770.8630.9880.8960.896
2014년 질병사망자수1.0000.7940.8570.9330.9420.9410.8940.9000.9100.7250.9411.0000.9510.8930.8880.9490.8620.9390.9660.892
2015년 질병사망자수1.0000.8050.8730.9850.9930.9740.9200.9140.9540.9040.9850.9511.0000.9150.9100.9900.9100.9880.9020.863
2016년 질병사망자수1.0000.9320.9750.8690.9170.9250.9710.9830.9850.9350.9040.8930.9151.0000.9950.9000.9630.8750.9390.960
2017년 질병사망자수1.0000.9400.9810.8700.9220.9280.9740.9790.9840.9370.8890.8880.9100.9951.0000.9060.9670.8760.9430.962
2018년 질병사망자수1.0000.8340.8690.9790.9930.9480.9030.9220.9130.8390.9770.9490.9900.9000.9061.0000.9290.9790.9160.885
2019년 질병사망자수1.0000.9350.9580.8570.9420.9180.9500.9650.9670.9650.8630.8620.9100.9630.9670.9291.0000.8680.9520.970
2020년 질병사망자수1.0000.7870.8470.9880.9830.9360.8960.9040.9140.8000.9880.9390.9880.8750.8760.9790.8681.0000.9000.914
2021년 질병사망자수1.0000.9240.9090.8830.9160.9070.9440.9480.9330.8930.8960.9660.9020.9390.9430.9160.9520.9001.0000.970
2022년 질병사망자수1.0000.9430.9580.8760.8760.9000.9550.9660.9570.9110.8960.8920.8630.9600.9620.8850.9700.9140.9701.000
2023-12-13T00:12:48.741705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2004년 질병사망자수2005년 질병사망자수2006년 질병사망자수2007년 질병사망자수2008년 질병사망자수2009년 질병사망자수2010년 질병사망자수2011년 질병사망자수2012년 질병사망자수2013년 질병사망자수2014년 질병사망자수2015년 질병사망자수2016년 질병사망자수2017년 질병사망자수2018년 질병사망자수2019년 질병사망자수2020년 질병사망자수2021년 질병사망자수2022년 질병사망자수
2004년 질병사망자수1.0000.9020.8510.7850.8190.7480.7800.7850.6920.7770.7870.6410.7540.7710.8140.7170.7520.7720.804
2005년 질병사망자수0.9021.0000.7840.7840.8250.8280.8020.7560.6380.7700.8010.6740.7560.7720.7530.7880.7280.7690.813
2006년 질병사망자수0.8510.7841.0000.7790.7610.7210.7230.7180.6570.7760.7010.6930.6590.6890.7470.7170.7150.7010.706
2007년 질병사망자수0.7850.7840.7791.0000.8490.8480.8270.7580.7920.7880.7890.7900.8160.7520.8310.8200.7920.7750.815
2008년 질병사망자수0.8190.8250.7610.8491.0000.8810.8680.8430.7590.8580.8580.7420.8250.7450.8400.8040.8220.7890.844
2009년 질병사망자수0.7480.8280.7210.8480.8811.0000.8530.7670.7290.8410.8350.7440.7510.7280.7900.8290.7680.7330.793
2010년 질병사망자수0.7800.8020.7230.8270.8680.8531.0000.7820.7860.8300.8160.7480.8200.7550.7990.7400.8030.8070.813
2011년 질병사망자수0.7850.7560.7180.7580.8430.7670.7821.0000.8090.8490.8570.7300.7920.7950.8110.7600.7750.8360.804
2012년 질병사망자수0.6920.6380.6570.7920.7590.7290.7860.8091.0000.8100.8730.7720.7620.7310.8040.7840.7620.8060.816
2013년 질병사망자수0.7770.7700.7760.7880.8580.8410.8300.8490.8101.0000.8730.7300.8090.6800.8180.7900.8330.7630.831
2014년 질병사망자수0.7870.8010.7010.7890.8580.8350.8160.8570.8730.8731.0000.8260.8320.7660.8150.7870.7530.8080.843
2015년 질병사망자수0.6410.6740.6930.7900.7420.7440.7480.7300.7720.7300.8261.0000.8150.7390.7940.7570.7200.7240.797
2016년 질병사망자수0.7540.7560.6590.8160.8250.7510.8200.7920.7620.8090.8320.8151.0000.7580.7780.7230.7550.7530.818
2017년 질병사망자수0.7710.7720.6890.7520.7450.7280.7550.7950.7310.6800.7660.7390.7581.0000.8170.8030.6840.8420.795
2018년 질병사망자수0.8140.7530.7470.8310.8400.7900.7990.8110.8040.8180.8150.7940.7780.8171.0000.8320.8370.7820.894
2019년 질병사망자수0.7170.7880.7170.8200.8040.8290.7400.7600.7840.7900.7870.7570.7230.8030.8321.0000.8050.8050.868
2020년 질병사망자수0.7520.7280.7150.7920.8220.7680.8030.7750.7620.8330.7530.7200.7550.6840.8370.8051.0000.8310.897
2021년 질병사망자수0.7720.7690.7010.7750.7890.7330.8070.8360.8060.7630.8080.7240.7530.8420.7820.8050.8311.0000.859
2022년 질병사망자수0.8040.8130.7060.8150.8440.7930.8130.8040.8160.8310.8430.7970.8180.7950.8940.8680.8970.8591.000

Missing values

2023-12-13T00:12:41.766369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:12:42.147519image/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 질병사망자수2005년 질병사망자수2006년 질병사망자수2007년 질병사망자수2008년 질병사망자수2009년 질병사망자수2010년 질병사망자수2011년 질병사망자수2012년 질병사망자수2013년 질병사망자수2014년 질병사망자수2015년 질병사망자수2016년 질병사망자수2017년 질병사망자수2018년 질병사망자수2019년 질병사망자수2020년 질병사망자수2021년 질병사망자수2022년 질병사망자수
0서 울 청38252925282621202130191733382927303745
1서울강남9449392628181812101414712131720243120
2서울동부39363022151310981111128182828171838
3서울서부242722108878212141312131219142220
4서울남부26311422161212861391116112019292136
5서울북부22181015117104775611181924151916
6서울관악19221416221481111151661010824111613
7의 정 부56482231331813162118272333424440424657
8강 원5578387225455626483
9태 백15616116313411811911711691117177144146147154140147105173
구분2004년 질병사망자수2005년 질병사망자수2006년 질병사망자수2007년 질병사망자수2008년 질병사망자수2009년 질병사망자수2010년 질병사망자수2011년 질병사망자수2012년 질병사망자수2013년 질병사망자수2014년 질병사망자수2015년 질병사망자수2016년 질병사망자수2017년 질병사망자수2018년 질병사망자수2019년 질병사망자수2020년 질병사망자수2021년 질병사망자수2022년 질병사망자수
39군 산6462331165532356274
40목 포1341454544610447691015610
41여 수181181211812107958861511271613
42제 주10056400531200364685
43대 전 청261922192512149137121512162522243123
44청 주17131718191085109131413101722211625
45충 주13161320282327141718242720183128251923
46천 안18137151415118141013109203026202325
47보 령42596770545656542270384445615741695960
48서 산0000000000000002242