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(I60-I64)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
Author공공데이터포털
URLhttps://www.data.go.kr/data/15120147/fileData.do

Alerts

5세 연령군 is highly overall correlated with 입원에피소드 발생건수High correlation
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 5세 연령군 and 1 other fieldsHigh correlation
발생 28일내 사망건수 is highly overall correlated with 입원에피소드 발생건수High correlation
광역시도명 is highly overall correlated with 광역시도High correlation
소득수준 has 821 (8.2%) zerosZeros
입원에피소드 발생건수 has 2486 (24.9%) zerosZeros
발생 28일내 사망건수 has 6453 (64.5%) zerosZeros

Reproduction

Analysis started2024-04-17 12:59:57.926236
Analysis finished2024-04-17 13:00:05.560675
Duration7.63 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%
Mean2009.7958
Minimum2006
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T22:00:05.609805image/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 deviation2.4580697
Coefficient of variation (CV)0.0012230445
Kurtosis-1.1781474
Mean2009.7958
Median Absolute Deviation (MAD)2
Skewness0.024902503
Sum20097958
Variance6.0421066
MonotonicityNot monotonic
2024-04-17T22:00:05.725342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2008 1201
12.0%
2012 1197
12.0%
2010 1185
11.8%
2013 1179
11.8%
2009 1176
11.8%
2011 1172
11.7%
2006 1146
11.5%
2007 1137
11.4%
2014 607
6.1%
ValueCountFrequency (%)
2006 1146
11.5%
2007 1137
11.4%
2008 1201
12.0%
2009 1176
11.8%
2010 1185
11.8%
2011 1172
11.7%
2012 1197
12.0%
2013 1179
11.8%
2014 607
6.1%
ValueCountFrequency (%)
2014 607
6.1%
2013 1179
11.8%
2012 1197
12.0%
2011 1172
11.7%
2010 1185
11.8%
2009 1176
11.8%
2008 1201
12.0%
2007 1137
11.4%
2006 1146
11.5%

성별
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5319 
2
4681 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 5319
53.2%
2 4681
46.8%

Length

2024-04-17T22:00:05.828914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:00:05.904396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5319
53.2%
2 4681
46.8%

5세 연령군
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.5305
Minimum20
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T22:00:05.976684image/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 deviation20.075218
Coefficient of variation (CV)0.38216308
Kurtosis-1.1937817
Mean52.5305
Median Absolute Deviation (MAD)15
Skewness-0.011874624
Sum525305
Variance403.01437
MonotonicityNot monotonic
2024-04-17T22:00:06.062989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
65 776
 
7.8%
45 747
 
7.5%
55 734
 
7.3%
20 726
 
7.3%
60 714
 
7.1%
50 711
 
7.1%
40 710
 
7.1%
85 710
 
7.1%
30 710
 
7.1%
25 707
 
7.1%
Other values (4) 2755
27.6%
ValueCountFrequency (%)
20 726
7.3%
25 707
7.1%
30 710
7.1%
35 658
6.6%
40 710
7.1%
45 747
7.5%
50 711
7.1%
55 734
7.3%
60 714
7.1%
65 776
7.8%
ValueCountFrequency (%)
85 710
7.1%
80 695
7.0%
75 695
7.0%
70 707
7.1%
65 776
7.8%
60 714
7.1%
55 734
7.3%
50 711
7.1%
45 747
7.5%
40 710
7.1%

소득수준
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.6494
Minimum0
Maximum99
Zeros821
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T22:00:06.164231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median11
Q313
95-th percentile99
Maximum99
Range99
Interquartile range (IQR)10

Descriptive statistics

Standard deviation25.373101
Coefficient of variation (CV)1.7320232
Kurtosis6.7697786
Mean14.6494
Median Absolute Deviation (MAD)6
Skewness2.8709715
Sum146494
Variance643.79426
MonotonicityNot monotonic
2024-04-17T22:00:06.270531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
14 878
8.8%
13 875
8.8%
1 842
8.4%
11 842
8.4%
3 834
8.3%
12 834
8.3%
2 832
8.3%
5 824
8.2%
15 822
8.2%
0 821
8.2%
Other values (2) 1596
16.0%
ValueCountFrequency (%)
0 821
8.2%
1 842
8.4%
2 832
8.3%
3 834
8.3%
4 799
8.0%
5 824
8.2%
11 842
8.4%
12 834
8.3%
13 875
8.8%
14 878
8.8%
ValueCountFrequency (%)
99 797
8.0%
15 822
8.2%
14 878
8.8%
13 875
8.8%
12 834
8.3%
11 842
8.4%
5 824
8.2%
4 799
8.0%
3 834
8.3%
2 832
8.3%

광역시도
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.6793
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T22:00:06.371195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile26
Q130
median42
Q345
95-th percentile48
Maximum50
Range39
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.9457618
Coefficient of variation (CV)0.23128034
Kurtosis0.38263156
Mean38.6793
Median Absolute Deviation (MAD)5
Skewness-0.9848357
Sum386793
Variance80.026654
MonotonicityNot monotonic
2024-04-17T22:00:06.467988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
41 870
 
8.7%
47 867
 
8.7%
45 864
 
8.6%
43 849
 
8.5%
44 760
 
7.6%
48 734
 
7.3%
42 581
 
5.8%
31 580
 
5.8%
28 562
 
5.6%
26 559
 
5.6%
Other values (7) 2774
27.7%
ValueCountFrequency (%)
11 272
 
2.7%
26 559
5.6%
27 551
5.5%
28 562
5.6%
29 284
 
2.8%
30 329
 
3.3%
31 580
5.8%
36 504
5.0%
41 870
8.7%
42 581
5.8%
ValueCountFrequency (%)
50 295
 
2.9%
48 734
7.3%
47 867
8.7%
46 539
5.4%
45 864
8.6%
44 760
7.6%
43 849
8.5%
42 581
5.8%
41 870
8.7%
36 504
5.0%

광역시도명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기도
870 
경상북도
867 
전라북도
864 
충청북도
849 
충청남도
760 
Other values (12)
5790 

Length

Max length7
Median length5
Mean length4.4083
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충청남도
2nd row인천광역시
3rd row서울특별시
4th row울산광역시
5th row경상북도

Common Values

ValueCountFrequency (%)
경기도 870
 
8.7%
경상북도 867
 
8.7%
전라북도 864
 
8.6%
충청북도 849
 
8.5%
충청남도 760
 
7.6%
경상남도 734
 
7.3%
강원도 581
 
5.8%
울산광역시 580
 
5.8%
인천광역시 562
 
5.6%
부산광역시 559
 
5.6%
Other values (7) 2774
27.7%

Length

2024-04-17T22:00:06.601160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 870
 
8.7%
경상북도 867
 
8.7%
전라북도 864
 
8.6%
충청북도 849
 
8.5%
충청남도 760
 
7.6%
경상남도 734
 
7.3%
강원도 581
 
5.8%
울산광역시 580
 
5.8%
인천광역시 562
 
5.6%
부산광역시 559
 
5.6%
Other values (7) 2774
27.7%

구시군
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
3626 
3
3610 
2
2764 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 3626
36.3%
3 3610
36.1%
2 2764
27.6%

Length

2024-04-17T22:00:06.713447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:00:06.796692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3626
36.3%
3 3610
36.1%
2 2764
27.6%

인년
Real number (ℝ)

HIGH CORRELATION 

Distinct9505
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3279.0276
Minimum0.027
Maximum83644.995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T22:00:06.897173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.027
5-th percentile63.383
Q1444.0825
median1269.485
Q33083.2807
95-th percentile12761.84
Maximum83644.995
Range83644.968
Interquartile range (IQR)2639.1982

Descriptive statistics

Standard deviation6736.823
Coefficient of variation (CV)2.0545186
Kurtosis40.5349
Mean3279.0276
Median Absolute Deviation (MAD)998.031
Skewness5.493542
Sum32790276
Variance45384785
MonotonicityNot monotonic
2024-04-17T22:00:07.023499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 14
 
0.1%
11.0 14
 
0.1%
3.0 10
 
0.1%
2.0 10
 
0.1%
5.0 10
 
0.1%
9.0 9
 
0.1%
7.0 9
 
0.1%
14.0 8
 
0.1%
4.0 8
 
0.1%
12.0 8
 
0.1%
Other values (9495) 9900
99.0%
ValueCountFrequency (%)
0.027 1
< 0.1%
0.047 1
< 0.1%
0.087 1
< 0.1%
0.09 1
< 0.1%
0.159 1
< 0.1%
0.205 1
< 0.1%
0.225 1
< 0.1%
0.238 1
< 0.1%
0.257 1
< 0.1%
0.258 1
< 0.1%
ValueCountFrequency (%)
83644.995 1
< 0.1%
81994.012 1
< 0.1%
81717.689 1
< 0.1%
80266.061 1
< 0.1%
80187.602 1
< 0.1%
78445.263 1
< 0.1%
77624.259 1
< 0.1%
76262.78 1
< 0.1%
75656.113 1
< 0.1%
75647.028 1
< 0.1%

실 인원수
Real number (ℝ)

HIGH CORRELATION 

Distinct4756
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3289.9487
Minimum1
Maximum83658
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T22:00:07.152381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile66
Q1453
median1283
Q33098.75
95-th percentile12787
Maximum83658
Range83657
Interquartile range (IQR)2645.75

Descriptive statistics

Standard deviation6740.3043
Coefficient of variation (CV)2.0487566
Kurtosis40.475678
Mean3289.9487
Median Absolute Deviation (MAD)1004
Skewness5.4890929
Sum32899487
Variance45431702
MonotonicityNot monotonic
2024-04-17T22:00:07.263349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 23
 
0.2%
3 23
 
0.2%
2 20
 
0.2%
5 16
 
0.2%
11 16
 
0.2%
4 15
 
0.1%
12 14
 
0.1%
283 14
 
0.1%
123 13
 
0.1%
7 13
 
0.1%
Other values (4746) 9833
98.3%
ValueCountFrequency (%)
1 23
0.2%
2 20
0.2%
3 23
0.2%
4 15
0.1%
5 16
0.2%
6 12
0.1%
7 13
0.1%
8 9
 
0.1%
9 12
0.1%
10 7
 
0.1%
ValueCountFrequency (%)
83658 1
< 0.1%
82009 1
< 0.1%
81746 1
< 0.1%
80295 1
< 0.1%
80202 1
< 0.1%
78472 1
< 0.1%
77640 1
< 0.1%
76285 1
< 0.1%
75663 1
< 0.1%
75660 1
< 0.1%

입원에피소드 발생건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct244
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.8116
Minimum0
Maximum493
Zeros2486
Zeros (%)24.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T22:00:07.386655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q320
95-th percentile84
Maximum493
Range493
Interquartile range (IQR)19

Descriptive statistics

Standard deviation36.524278
Coefficient of variation (CV)1.9415828
Kurtosis26.914109
Mean18.8116
Median Absolute Deviation (MAD)5
Skewness4.2920187
Sum188116
Variance1334.0229
MonotonicityNot monotonic
2024-04-17T22:00:07.532090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2486
24.9%
1 866
 
8.7%
2 587
 
5.9%
3 451
 
4.5%
4 375
 
3.8%
5 323
 
3.2%
6 285
 
2.9%
8 265
 
2.6%
7 237
 
2.4%
9 213
 
2.1%
Other values (234) 3912
39.1%
ValueCountFrequency (%)
0 2486
24.9%
1 866
 
8.7%
2 587
 
5.9%
3 451
 
4.5%
4 375
 
3.8%
5 323
 
3.2%
6 285
 
2.9%
7 237
 
2.4%
8 265
 
2.6%
9 213
 
2.1%
ValueCountFrequency (%)
493 1
< 0.1%
472 1
< 0.1%
455 1
< 0.1%
394 1
< 0.1%
375 1
< 0.1%
374 2
< 0.1%
363 1
< 0.1%
360 1
< 0.1%
345 1
< 0.1%
332 1
< 0.1%

발생 28일내 사망건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9544
Minimum0
Maximum37
Zeros6453
Zeros (%)64.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T22:00:07.634520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum37
Range37
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.0878114
Coefficient of variation (CV)2.1875644
Kurtosis29.780132
Mean0.9544
Median Absolute Deviation (MAD)0
Skewness4.3032929
Sum9544
Variance4.3589565
MonotonicityNot monotonic
2024-04-17T22:00:07.744782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 6453
64.5%
1 1636
 
16.4%
2 740
 
7.4%
3 378
 
3.8%
4 239
 
2.4%
5 144
 
1.4%
6 114
 
1.1%
7 81
 
0.8%
8 61
 
0.6%
9 41
 
0.4%
Other values (15) 113
 
1.1%
ValueCountFrequency (%)
0 6453
64.5%
1 1636
 
16.4%
2 740
 
7.4%
3 378
 
3.8%
4 239
 
2.4%
5 144
 
1.4%
6 114
 
1.1%
7 81
 
0.8%
8 61
 
0.6%
9 41
 
0.4%
ValueCountFrequency (%)
37 1
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 2
 
< 0.1%
21 3
< 0.1%
19 1
 
< 0.1%
18 3
< 0.1%
17 5
0.1%
16 6
0.1%
15 5
0.1%

Interactions

2024-04-17T22:00:04.612589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:59:59.439657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:00.193423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:00.863917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:01.572937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:02.324913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:03.002652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:03.655842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:04.703352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:59:59.541378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:00.286983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:00.955494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:01.681074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:02.426997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:03.090349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:03.744652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:04.786599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:59:59.637370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:00.371895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:01.047891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:01.767856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:02.520795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:03.167167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:03.824262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:04.882091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:59:59.738873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:00.446326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:01.131054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:01.859998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:02.600787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:03.241508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:03.898629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:04.992204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:59:59.840863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:00.535244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:01.220690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:01.968069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:02.691279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:03.327104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:03.992731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:05.074589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:59:59.926426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:00.609052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:01.291933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:02.056819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:02.777172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:03.401078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:04.065978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:05.152881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:00.005443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:00.694620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:01.377473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:02.147020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:02.848541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:03.471148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:04.142777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:05.230138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:00.101889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:00.772074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:01.465263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:02.232187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:02.921413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:03.549917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:00:04.515311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T22:00:07.842770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생년도성별5세 연령군소득수준광역시도광역시도명구시군인년실 인원수입원에피소드 발생건수발생 28일내 사망건수
발생년도1.0000.3350.0330.0000.0710.0760.0700.0190.0200.0000.046
성별0.3351.0000.0000.0000.0000.0000.0000.0000.0000.0340.046
5세 연령군0.0330.0001.0000.0000.0000.0000.0000.2180.2180.2260.232
소득수준0.0000.0000.0001.0000.0000.0000.0520.1880.1880.1890.130
광역시도0.0710.0000.0000.0001.0001.0000.4940.4540.4530.3420.383
광역시도명0.0760.0000.0000.0001.0001.0000.6440.5040.5040.4100.333
구시군0.0700.0000.0000.0520.4940.6441.0000.3200.3200.2760.149
인년0.0190.0000.2180.1880.4540.5040.3201.0001.0000.2530.122
실 인원수0.0200.0000.2180.1880.4530.5040.3201.0001.0000.2540.123
입원에피소드 발생건수0.0000.0340.2260.1890.3420.4100.2760.2530.2541.0000.617
발생 28일내 사망건수0.0460.0460.2320.1300.3830.3330.1490.1220.1230.6171.000
2024-04-17T22:00:07.945463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구시군성별광역시도명
구시군1.0000.0000.445
성별0.0001.0000.000
광역시도명0.4450.0001.000
2024-04-17T22:00:08.024688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생년도5세 연령군소득수준광역시도인년실 인원수입원에피소드 발생건수발생 28일내 사망건수성별광역시도명구시군
발생년도1.000-0.0090.0040.0100.0330.032-0.013-0.0640.2370.0360.042
5세 연령군-0.0091.0000.0160.012-0.359-0.3520.5280.4620.0000.0000.000
소득수준0.0040.0161.000-0.0150.1080.107-0.169-0.0900.0000.0000.015
광역시도0.0100.012-0.0151.0000.0180.0180.027-0.0140.0001.0000.361
인년0.033-0.3590.1080.0181.0001.0000.3520.2200.0000.2220.202
실 인원수0.032-0.3520.1070.0181.0001.0000.3570.2260.0000.2220.202
입원에피소드 발생건수-0.0130.528-0.1690.0270.3520.3571.0000.7100.0340.1760.126
발생 28일내 사망건수-0.0640.462-0.090-0.0140.2200.2260.7101.0000.0350.1460.094
성별0.2370.0000.0000.0000.0000.0000.0340.0351.0000.0000.000
광역시도명0.0360.0000.0001.0000.2220.2220.1760.1460.0001.0000.445
구시군0.0420.0000.0150.3610.2020.2020.1260.0940.0000.4451.000

Missing values

2024-04-17T22:00:05.345750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T22:00:05.491588image/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
951720062601544충청남도31095.6681099110
1645520071751428인천광역시11667.7471721655
252802008145311서울특별시133523.95833570983
3763820091501331울산광역시12638.8062642100
3314520082659947경상북도2816.11881900
4950620101501430대전광역시15262.8365272181
6163920111501542강원도31224.0122420
605132011140329광주광역시15679.163568592
5261720101859945전라북도3107.82411700
5379220102301341경기도31139.406114010
발생년도성별5세 연령군소득수준광역시도광역시도명구시군인년실 인원수입원에피소드 발생건수발생 28일내 사망건수
448620061701428인천광역시3324.19732940
854412013150430대전광역시14375.056438570
9401920132801436세종특별자치시1244.76224950
772482012225126부산광역시3128.012800
739082012155426부산광역시3283.01628430
1648120071751448경상남도22840.14292613614
2557520081459941경기도3502.050200
5129920101709926부산광역시11174.129120000
664842011240130대전광역시15151.3225158120
980972014160547경상북도24100.6924110230