Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory106.0 B

Variable types

Numeric8
Categorical3

Dataset

Descriptiono 모집단: 해당년도 만 20세 이상 국민 건강보험 가입자 또는 의료급여수급권자(외국인 포함) o 내용: 해당년도 주·부상병 조건(I20-I25)의 만 20세 이상 환자의 진료에피소드 및 사망건수 o 진료에피소드 구축기준 - 진료개시일 간 간격이 28일 이내인 경우 동일한 진료에피소드로 간주함 - 선별조건 ① 1일 이상 입원 또는 1일 내 사망 또는 응급실 방문 또는 (최초발생 7일 내) 기관 간 전원 ② (최초발생 7일 내) 주 이용기관이 급성기병의원(요양병원 제외)인 경우 ③ (최초발생 7일 내) 주 이용기관에서 주·부상병 조건인 경우
URLhttps://www.data.go.kr/data/15119957/fileData.do

Alerts

광역시도 is highly overall correlated with 광역시도명High correlation
인년 is highly overall correlated with 실 인원수High correlation
실 인원수 is highly overall correlated with 인년High correlation
입원에피소드 발생건수 is highly overall correlated with 발생 28일내 사망건수High correlation
발생 28일내 사망건수 is highly overall correlated with 입원에피소드 발생건수High correlation
광역시도명 is highly overall correlated with 광역시도High correlation
소득수준 has 800 (8.0%) zerosZeros
입원에피소드 발생건수 has 2105 (21.1%) zerosZeros
발생 28일내 사망건수 has 7384 (73.8%) zerosZeros

Reproduction

Analysis started2023-12-12 02:05:15.003024
Analysis finished2023-12-12 02:05:25.818558
Duration10.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

발생년도
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.8102
Minimum2006
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:05:25.880185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2006
Q12008
median2010
Q32012
95-th percentile2014
Maximum2014
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4550313
Coefficient of variation (CV)0.001221524
Kurtosis-1.162395
Mean2009.8102
Median Absolute Deviation (MAD)2
Skewness0.004108151
Sum20098102
Variance6.0271787
MonotonicityNot monotonic
2023-12-12T11:05:26.010074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2011 1262
12.6%
2010 1233
12.3%
2013 1198
12.0%
2006 1174
11.7%
2009 1159
11.6%
2008 1140
11.4%
2012 1131
11.3%
2007 1099
11.0%
2014 604
6.0%
ValueCountFrequency (%)
2006 1174
11.7%
2007 1099
11.0%
2008 1140
11.4%
2009 1159
11.6%
2010 1233
12.3%
2011 1262
12.6%
2012 1131
11.3%
2013 1198
12.0%
2014 604
6.0%
ValueCountFrequency (%)
2014 604
6.0%
2013 1198
12.0%
2012 1131
11.3%
2011 1262
12.6%
2010 1233
12.3%
2009 1159
11.6%
2008 1140
11.4%
2007 1099
11.0%
2006 1174
11.7%

성별
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5264 
2
4736 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 5264
52.6%
2 4736
47.4%

Length

2023-12-12T11:05:26.180495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:05:26.286919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5264
52.6%
2 4736
47.4%

5세 연령군
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.531
Minimum20
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:05:26.402385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q135
median55
Q370
95-th percentile85
Maximum85
Range65
Interquartile range (IQR)35

Descriptive statistics

Standard deviation20.244135
Coefficient of variation (CV)0.38537503
Kurtosis-1.2314919
Mean52.531
Median Absolute Deviation (MAD)20
Skewness-0.0078856025
Sum525310
Variance409.82502
MonotonicityNot monotonic
2023-12-12T11:05:26.560189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
80 760
 
7.6%
30 746
 
7.5%
70 726
 
7.3%
75 722
 
7.2%
20 721
 
7.2%
35 719
 
7.2%
65 717
 
7.2%
50 717
 
7.2%
25 716
 
7.2%
55 707
 
7.1%
Other values (4) 2749
27.5%
ValueCountFrequency (%)
20 721
7.2%
25 716
7.2%
30 746
7.5%
35 719
7.2%
40 678
6.8%
45 688
6.9%
50 717
7.2%
55 707
7.1%
60 692
6.9%
65 717
7.2%
ValueCountFrequency (%)
85 691
6.9%
80 760
7.6%
75 722
7.2%
70 726
7.3%
65 717
7.2%
60 692
6.9%
55 707
7.1%
50 717
7.2%
45 688
6.9%
40 678
6.8%

소득수준
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.6205
Minimum0
Maximum99
Zeros800
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:05:26.714156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q314
95-th percentile99
Maximum99
Range99
Interquartile range (IQR)12

Descriptive statistics

Standard deviation25.479886
Coefficient of variation (CV)1.7427507
Kurtosis6.6924762
Mean14.6205
Median Absolute Deviation (MAD)5
Skewness2.8593656
Sum146205
Variance649.2246
MonotonicityNot monotonic
2023-12-12T11:05:26.849016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 876
8.8%
1 871
8.7%
14 865
8.6%
5 858
8.6%
15 852
8.5%
3 840
8.4%
11 825
8.2%
13 806
8.1%
99 803
8.0%
4 802
8.0%
Other values (2) 1602
16.0%
ValueCountFrequency (%)
0 800
8.0%
1 871
8.7%
2 876
8.8%
3 840
8.4%
4 802
8.0%
5 858
8.6%
11 825
8.2%
12 802
8.0%
13 806
8.1%
14 865
8.6%
ValueCountFrequency (%)
99 803
8.0%
15 852
8.5%
14 865
8.6%
13 806
8.1%
12 802
8.0%
11 825
8.2%
5 858
8.6%
4 802
8.0%
3 840
8.4%
2 876
8.8%

광역시도
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.6583
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:05:26.985575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile26
Q130
median42
Q346
95-th percentile48
Maximum50
Range39
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.0675886
Coefficient of variation (CV)0.23455736
Kurtosis0.44127509
Mean38.6583
Median Absolute Deviation (MAD)5
Skewness-1.0116249
Sum386583
Variance82.221163
MonotonicityNot monotonic
2023-12-12T11:05:27.136957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
47 902
 
9.0%
41 882
 
8.8%
43 876
 
8.8%
45 781
 
7.8%
44 770
 
7.7%
48 738
 
7.4%
28 580
 
5.8%
42 574
 
5.7%
27 561
 
5.6%
46 551
 
5.5%
Other values (7) 2785
27.9%
ValueCountFrequency (%)
11 300
 
3.0%
26 543
5.4%
27 561
5.6%
28 580
5.8%
29 300
 
3.0%
30 296
 
3.0%
31 545
5.5%
36 491
4.9%
41 882
8.8%
42 574
5.7%
ValueCountFrequency (%)
50 310
 
3.1%
48 738
7.4%
47 902
9.0%
46 551
5.5%
45 781
7.8%
44 770
7.7%
43 876
8.8%
42 574
5.7%
41 882
8.8%
36 491
4.9%

광역시도명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경상북도
902 
경기도
882 
충청북도
876 
전라북도
781 
충청남도
770 
Other values (12)
5789 

Length

Max length7
Median length5
Mean length4.4072
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상남도
2nd row울산광역시
3rd row인천광역시
4th row경기도
5th row충청남도

Common Values

ValueCountFrequency (%)
경상북도 902
 
9.0%
경기도 882
 
8.8%
충청북도 876
 
8.8%
전라북도 781
 
7.8%
충청남도 770
 
7.7%
경상남도 738
 
7.4%
인천광역시 580
 
5.8%
강원도 574
 
5.7%
대구광역시 561
 
5.6%
전라남도 551
 
5.5%
Other values (7) 2785
27.9%

Length

2023-12-12T11:05:27.315643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 902
 
9.0%
경기도 882
 
8.8%
충청북도 876
 
8.8%
전라북도 781
 
7.8%
충청남도 770
 
7.7%
경상남도 738
 
7.4%
인천광역시 580
 
5.8%
강원도 574
 
5.7%
대구광역시 561
 
5.6%
전라남도 551
 
5.5%
Other values (7) 2785
27.9%

구시군
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
3638 
3
3572 
2
2790 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 3638
36.4%
3 3572
35.7%
2 2790
27.9%

Length

2023-12-12T11:05:27.477088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:05:27.621683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3638
36.4%
3 3572
35.7%
2 2790
27.9%

인년
Real number (ℝ)

HIGH CORRELATION 

Distinct9529
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3380.922
Minimum0.093
Maximum98104.218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:05:27.779261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.093
5-th percentile62.9915
Q1434.7975
median1243.876
Q33055.594
95-th percentile13633.772
Maximum98104.218
Range98104.125
Interquartile range (IQR)2620.7965

Descriptive statistics

Standard deviation7193.3518
Coefficient of variation (CV)2.1276302
Kurtosis45.386144
Mean3380.922
Median Absolute Deviation (MAD)975.9235
Skewness5.7593113
Sum33809220
Variance51744310
MonotonicityNot monotonic
2023-12-12T11:05:28.332417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.0 14
 
0.1%
1.0 13
 
0.1%
15.0 12
 
0.1%
8.0 11
 
0.1%
11.0 11
 
0.1%
9.0 10
 
0.1%
18.0 10
 
0.1%
13.0 7
 
0.1%
3.0 7
 
0.1%
6.0 7
 
0.1%
Other values (9519) 9898
99.0%
ValueCountFrequency (%)
0.093 1
< 0.1%
0.104 2
< 0.1%
0.118 1
< 0.1%
0.123 1
< 0.1%
0.137 1
< 0.1%
0.156 1
< 0.1%
0.304 1
< 0.1%
0.373 1
< 0.1%
0.414 1
< 0.1%
0.43 1
< 0.1%
ValueCountFrequency (%)
98104.218 1
< 0.1%
92683.054 1
< 0.1%
90546.683 1
< 0.1%
88076.526 1
< 0.1%
87535.447 1
< 0.1%
87326.663 1
< 0.1%
86960.677 1
< 0.1%
86073.404 1
< 0.1%
85211.755 1
< 0.1%
83644.995 1
< 0.1%

실 인원수
Real number (ℝ)

HIGH CORRELATION 

Distinct4728
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3391.749
Minimum1
Maximum98126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:05:28.507373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile66
Q1441.75
median1257
Q33077.25
95-th percentile13647.15
Maximum98126
Range98125
Interquartile range (IQR)2635.5

Descriptive statistics

Standard deviation7196.6452
Coefficient of variation (CV)2.1218095
Kurtosis45.32274
Mean3391.749
Median Absolute Deviation (MAD)980.5
Skewness5.7548555
Sum33917490
Variance51791702
MonotonicityNot monotonic
2023-12-12T11:05:28.716174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 25
 
0.2%
1 20
 
0.2%
4 17
 
0.2%
3 16
 
0.2%
15 16
 
0.2%
106 15
 
0.1%
45 14
 
0.1%
11 13
 
0.1%
9 13
 
0.1%
8 12
 
0.1%
Other values (4718) 9839
98.4%
ValueCountFrequency (%)
1 20
0.2%
2 25
0.2%
3 16
0.2%
4 17
0.2%
5 11
0.1%
6 11
0.1%
7 7
 
0.1%
8 12
0.1%
9 13
0.1%
10 7
 
0.1%
ValueCountFrequency (%)
98126 1
< 0.1%
92700 1
< 0.1%
90562 1
< 0.1%
88084 1
< 0.1%
87545 1
< 0.1%
87360 1
< 0.1%
86969 1
< 0.1%
86090 1
< 0.1%
85230 1
< 0.1%
83658 1
< 0.1%

입원에피소드 발생건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct242
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.8093
Minimum0
Maximum494
Zeros2105
Zeros (%)21.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:05:28.923236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q319
95-th percentile73
Maximum494
Range494
Interquartile range (IQR)18

Descriptive statistics

Standard deviation35.676595
Coefficient of variation (CV)2.0032564
Kurtosis42.458365
Mean17.8093
Median Absolute Deviation (MAD)6
Skewness5.3157835
Sum178093
Variance1272.8194
MonotonicityNot monotonic
2023-12-12T11:05:29.138127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2105
21.1%
1 840
 
8.4%
2 666
 
6.7%
3 474
 
4.7%
4 464
 
4.6%
5 349
 
3.5%
6 306
 
3.1%
7 288
 
2.9%
8 251
 
2.5%
9 250
 
2.5%
Other values (232) 4007
40.1%
ValueCountFrequency (%)
0 2105
21.1%
1 840
 
8.4%
2 666
 
6.7%
3 474
 
4.7%
4 464
 
4.6%
5 349
 
3.5%
6 306
 
3.1%
7 288
 
2.9%
8 251
 
2.5%
9 250
 
2.5%
ValueCountFrequency (%)
494 1
< 0.1%
492 1
< 0.1%
490 1
< 0.1%
463 1
< 0.1%
456 1
< 0.1%
452 1
< 0.1%
450 1
< 0.1%
442 1
< 0.1%
440 1
< 0.1%
415 1
< 0.1%

발생 28일내 사망건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6032
Minimum0
Maximum25
Zeros7384
Zeros (%)73.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:05:29.288800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum25
Range25
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4993247
Coefficient of variation (CV)2.4856179
Kurtosis31.81693
Mean0.6032
Median Absolute Deviation (MAD)0
Skewness4.5636755
Sum6032
Variance2.2479746
MonotonicityNot monotonic
2023-12-12T11:05:29.438004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 7384
73.8%
1 1343
 
13.4%
2 514
 
5.1%
3 306
 
3.1%
4 172
 
1.7%
5 82
 
0.8%
6 54
 
0.5%
7 50
 
0.5%
8 30
 
0.3%
9 25
 
0.2%
Other values (9) 40
 
0.4%
ValueCountFrequency (%)
0 7384
73.8%
1 1343
 
13.4%
2 514
 
5.1%
3 306
 
3.1%
4 172
 
1.7%
5 82
 
0.8%
6 54
 
0.5%
7 50
 
0.5%
8 30
 
0.3%
9 25
 
0.2%
ValueCountFrequency (%)
25 1
 
< 0.1%
18 3
 
< 0.1%
17 2
 
< 0.1%
16 2
 
< 0.1%
14 4
 
< 0.1%
13 3
 
< 0.1%
12 4
 
< 0.1%
11 8
 
0.1%
10 13
0.1%
9 25
0.2%

Interactions

2023-12-12T11:05:24.573192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:17.236291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:18.390031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:19.431809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:20.331522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:21.586508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:22.612319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:23.599812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:24.707354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:17.378516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:18.534656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:19.572081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:20.454542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:21.724994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:22.756878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:23.749447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:24.821829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:17.522354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:18.655315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:19.696617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:20.886251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:21.834609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:22.878800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:23.862868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:24.932666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:17.676105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:18.763983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:19.806130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:21.012966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:21.967296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:23.003970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:23.985498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:25.093301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:17.837856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:18.907326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:19.931661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:21.142880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:22.126918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:23.141419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:24.135765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:25.214582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:17.957336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:19.036509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:20.037445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:21.243433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:22.269458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:23.248185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:24.258210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:25.314634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:18.096806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:19.177064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:20.134333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:21.348595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:22.389721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:23.361198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:24.365049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:25.425664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:18.238273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:19.298022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:20.232442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:21.467120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:22.490453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:23.475854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:24.464503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:05:29.555508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생년도성별5세 연령군소득수준광역시도광역시도명구시군인년실 인원수입원에피소드 발생건수발생 28일내 사망건수
발생년도1.0000.3390.0440.0230.0580.0490.0490.0180.0170.0170.031
성별0.3391.0000.0320.0000.0050.0000.0000.0000.0000.0700.041
5세 연령군0.0440.0321.0000.0000.0000.0000.0210.2290.2290.2510.235
소득수준0.0230.0000.0001.0000.0210.0320.0190.1580.1580.1670.162
광역시도0.0580.0050.0000.0211.0001.0000.4970.4450.4450.3490.258
광역시도명0.0490.0000.0000.0321.0001.0000.6450.4970.4980.3970.303
구시군0.0490.0000.0210.0190.4970.6451.0000.2860.2870.2220.195
인년0.0180.0000.2290.1580.4450.4970.2861.0001.0000.4620.062
실 인원수0.0170.0000.2290.1580.4450.4980.2871.0001.0000.4640.069
입원에피소드 발생건수0.0170.0700.2510.1670.3490.3970.2220.4620.4641.0000.530
발생 28일내 사망건수0.0310.0410.2350.1620.2580.3030.1950.0620.0690.5301.000
2023-12-12T11:05:29.743632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구시군성별광역시도명
구시군1.0000.0000.446
성별0.0001.0000.000
광역시도명0.4460.0001.000
2023-12-12T11:05:29.874825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생년도5세 연령군소득수준광역시도인년실 인원수입원에피소드 발생건수발생 28일내 사망건수성별광역시도명구시군
발생년도1.000-0.0150.0020.0160.0580.0580.021-0.0350.2390.0270.034
5세 연령군-0.0151.000-0.0020.021-0.386-0.3790.4070.4490.0250.0000.012
소득수준0.002-0.0021.000-0.0060.0960.096-0.120-0.0800.0000.0170.006
광역시도0.0160.021-0.0061.0000.0090.0090.011-0.0050.0041.0000.364
인년0.058-0.3860.0960.0091.0001.0000.4670.1360.0000.2180.178
실 인원수0.058-0.3790.0960.0091.0001.0000.4720.1420.0000.2190.179
입원에피소드 발생건수0.0210.407-0.1200.0110.4670.4721.0000.5700.0540.1660.135
발생 28일내 사망건수-0.0350.449-0.080-0.0050.1360.1420.5701.0000.0390.1260.087
성별0.2390.0250.0000.0040.0000.0000.0540.0391.0000.0000.000
광역시도명0.0270.0000.0171.0000.2180.2190.1660.1260.0001.0000.446
구시군0.0340.0120.0060.3640.1780.1790.1350.0870.0000.4461.000

Missing values

2023-12-12T11:05:25.576437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:05:25.741051image/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

발생년도성별5세 연령군소득수준광역시도광역시도명구시군인년실 인원수입원에피소드 발생건수발생 28일내 사망건수
66962006230048경상남도3385.72638700
3431420082801431울산광역시3182.98918950
921582013260428인천광역시13527.3453531190
224422007280541경기도12393.3442456546
97592006265544충청남도22258.2872262301
9225120132601147경상북도11646.963165080
841022013135150제주특별자치도21766.028176961
479482010135148경상남도31427.708143020
683082011260348경상남도11057.694106020
87882006255143충청북도1637.95664090
발생년도성별5세 연령군소득수준광역시도광역시도명구시군인년실 인원수입원에피소드 발생건수발생 28일내 사망건수
274532008170527대구광역시11894.3931919463
971702014150341경기도115520.31915564630
5942420111251348경상남도32777.685277820
76612006240448경상남도210401.29710405100
9950920141751528인천광역시3270.65227550
866812013165345전라북도1476.239479140
5345620102259926부산광역시3105.010500
420842009235342강원도23688.543369010
9436520132851229광주광역시1716.6276551
4515720092701231울산광역시1556.03956170