Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory839.8 KiB
Average record size in memory86.0 B

Variable types

Categorical3
Text1
Numeric5

Dataset

Description3단상병별 건강보험 진료비 통계 / 진료일자 기준(심사분은 각 진료년+4개월) (예) 진료년월: 2022.1월~12월, 심사년월: 2022.1월~2023.4월 / 보험자: 건강보험 / 요양기관 종별: 약국 제외 / 한방상병 제외
Author건강보험심사평가원
URLhttps://www.data.go.kr/data/15072907/fileData.do

Alerts

진료년도 has constant value ""Constant
환자수 is highly overall correlated with 명세서청구건수 and 3 other fieldsHigh correlation
명세서청구건수 is highly overall correlated with 환자수 and 3 other fieldsHigh correlation
입내원일수 is highly overall correlated with 환자수 and 3 other fieldsHigh correlation
요양급여비용총액 is highly overall correlated with 환자수 and 3 other fieldsHigh correlation
보험자부담금 is highly overall correlated with 환자수 and 3 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 15:36:03.605567
Analysis finished2023-12-12 15:36:08.790265
Duration5.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

진료년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 10000
100.0%

Length

2023-12-13T00:36:08.909563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:36:09.054392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 10000
100.0%

주상병코드
Text

Distinct1629
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T00:36:09.615567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters32
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)0.8%

Sample

1st rowH36
2nd rowT17
3rd rowA01
4th rowI26
5th rowL05
ValueCountFrequency (%)
l74 15
 
0.1%
f32 15
 
0.1%
c84 15
 
0.1%
k00 15
 
0.1%
k76 14
 
0.1%
c30 14
 
0.1%
h36 14
 
0.1%
f20 14
 
0.1%
m54 13
 
0.1%
t95 13
 
0.1%
Other values (1619) 9858
98.6%
2023-12-13T00:36:10.438525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2384
 
7.9%
2 2220
 
7.4%
1 2192
 
7.3%
3 2060
 
6.9%
4 2017
 
6.7%
6 1960
 
6.5%
5 1909
 
6.4%
8 1850
 
6.2%
7 1753
 
5.8%
9 1655
 
5.5%
Other values (22) 10000
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20000
66.7%
Uppercase Letter 10000
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 672
 
6.7%
T 661
 
6.6%
R 632
 
6.3%
L 548
 
5.5%
M 539
 
5.4%
Z 534
 
5.3%
Q 532
 
5.3%
D 524
 
5.2%
K 513
 
5.1%
H 506
 
5.1%
Other values (12) 4339
43.4%
Decimal Number
ValueCountFrequency (%)
0 2384
11.9%
2 2220
11.1%
1 2192
11.0%
3 2060
10.3%
4 2017
10.1%
6 1960
9.8%
5 1909
9.5%
8 1850
9.2%
7 1753
8.8%
9 1655
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common 20000
66.7%
Latin 10000
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 672
 
6.7%
T 661
 
6.6%
R 632
 
6.3%
L 548
 
5.5%
M 539
 
5.4%
Z 534
 
5.3%
Q 532
 
5.3%
D 524
 
5.2%
K 513
 
5.1%
H 506
 
5.1%
Other values (12) 4339
43.4%
Common
ValueCountFrequency (%)
0 2384
11.9%
2 2220
11.1%
1 2192
11.0%
3 2060
10.3%
4 2017
10.1%
6 1960
9.8%
5 1909
9.5%
8 1850
9.2%
7 1753
8.8%
9 1655
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2384
 
7.9%
2 2220
 
7.4%
1 2192
 
7.3%
3 2060
 
6.9%
4 2017
 
6.7%
6 1960
 
6.5%
5 1909
 
6.4%
8 1850
 
6.2%
7 1753
 
5.8%
9 1655
 
5.5%
Other values (22) 10000
33.3%

성별
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
여
5136 
남
4864 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row여
2nd row남
3rd row여
4th row여
5th row남

Common Values

ValueCountFrequency (%)
여 5136
51.4%
남 4864
48.6%

Length

2023-12-13T00:36:10.660945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:36:10.824368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여 5136
51.4%
남 4864
48.6%

연령군
Categorical

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
06_25~29세
 
609
05_20~24세
 
597
12_55~59세
 
590
07_30~34세
 
589
13_60~64세
 
576
Other values (13)
7039 

Length

Max length9
Median length9
Mean length8.7894
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row17_80~84세
2nd row17_80~84세
3rd row09_40~44세
4th row12_55~59세
5th row14_65~69세

Common Values

ValueCountFrequency (%)
06_25~29세 609
 
6.1%
05_20~24세 597
 
6.0%
12_55~59세 590
 
5.9%
07_30~34세 589
 
5.9%
13_60~64세 576
 
5.8%
09_40~44세 572
 
5.7%
11_50~54세 569
 
5.7%
04_15~19세 569
 
5.7%
15_70~74세 557
 
5.6%
08_35~39세 544
 
5.4%
Other values (8) 4228
42.3%

Length

2023-12-13T00:36:11.035511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
06_25~29세 609
 
5.8%
05_20~24세 597
 
5.7%
12_55~59세 590
 
5.6%
07_30~34세 589
 
5.6%
13_60~64세 576
 
5.5%
09_40~44세 572
 
5.4%
11_50~54세 569
 
5.4%
04_15~19세 569
 
5.4%
15_70~74세 557
 
5.3%
08_35~39세 544
 
5.2%
Other values (9) 4768
45.2%

환자수
Real number (ℝ)

HIGH CORRELATION 

Distinct3460
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6855.1205
Minimum1
Maximum861086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:36:11.248646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q116
median144
Q31517.5
95-th percentile29019.95
Maximum861086
Range861085
Interquartile range (IQR)1501.5

Descriptive statistics

Standard deviation34566.522
Coefficient of variation (CV)5.0424382
Kurtosis217.25858
Mean6855.1205
Median Absolute Deviation (MAD)142
Skewness12.688876
Sum68551205
Variance1.1948444 × 109
MonotonicityNot monotonic
2023-12-13T00:36:11.479594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 519
 
5.2%
2 344
 
3.4%
3 233
 
2.3%
4 195
 
1.9%
5 174
 
1.7%
8 132
 
1.3%
6 123
 
1.2%
7 114
 
1.1%
11 109
 
1.1%
10 99
 
1.0%
Other values (3450) 7958
79.6%
ValueCountFrequency (%)
1 519
5.2%
2 344
3.4%
3 233
2.3%
4 195
 
1.9%
5 174
 
1.7%
6 123
 
1.2%
7 114
 
1.1%
8 132
 
1.3%
9 94
 
0.9%
10 99
 
1.0%
ValueCountFrequency (%)
861086 1
< 0.1%
813395 1
< 0.1%
771310 1
< 0.1%
745896 1
< 0.1%
723913 1
< 0.1%
666005 1
< 0.1%
621963 1
< 0.1%
618629 1
< 0.1%
580203 1
< 0.1%
543716 1
< 0.1%

명세서청구건수
Real number (ℝ)

HIGH CORRELATION 

Distinct4328
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17978.29
Minimum1
Maximum3814990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:36:11.669666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q139
median400
Q33781.5
95-th percentile66384.75
Maximum3814990
Range3814989
Interquartile range (IQR)3742.5

Descriptive statistics

Standard deviation114249.96
Coefficient of variation (CV)6.3548844
Kurtosis440.42637
Mean17978.29
Median Absolute Deviation (MAD)396
Skewness18.278716
Sum1.797829 × 108
Variance1.3053053 × 1010
MonotonicityNot monotonic
2023-12-13T00:36:11.873417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 345
 
3.5%
2 247
 
2.5%
3 152
 
1.5%
4 138
 
1.4%
5 117
 
1.2%
6 102
 
1.0%
9 85
 
0.9%
8 80
 
0.8%
13 75
 
0.8%
7 73
 
0.7%
Other values (4318) 8586
85.9%
ValueCountFrequency (%)
1 345
3.5%
2 247
2.5%
3 152
1.5%
4 138
 
1.4%
5 117
 
1.2%
6 102
 
1.0%
7 73
 
0.7%
8 80
 
0.8%
9 85
 
0.9%
10 65
 
0.7%
ValueCountFrequency (%)
3814990 1
< 0.1%
3261857 1
< 0.1%
3230307 1
< 0.1%
3193769 1
< 0.1%
2972601 1
< 0.1%
2535156 1
< 0.1%
2056207 1
< 0.1%
1914910 1
< 0.1%
1885429 1
< 0.1%
1867028 1
< 0.1%

입내원일수
Real number (ℝ)

HIGH CORRELATION 

Distinct4634
Distinct (%)46.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19891.686
Minimum0
Maximum3828039
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:36:12.042862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q151
median558
Q34813
95-th percentile77211.5
Maximum3828039
Range3828039
Interquartile range (IQR)4762

Descriptive statistics

Standard deviation118952.47
Coefficient of variation (CV)5.9800092
Kurtosis402.19423
Mean19891.686
Median Absolute Deviation (MAD)552
Skewness17.396686
Sum1.9891686 × 108
Variance1.414969 × 1010
MonotonicityNot monotonic
2023-12-13T00:36:12.200277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 328
 
3.3%
2 225
 
2.2%
3 126
 
1.3%
4 120
 
1.2%
5 94
 
0.9%
6 80
 
0.8%
9 78
 
0.8%
8 77
 
0.8%
7 72
 
0.7%
10 59
 
0.6%
Other values (4624) 8741
87.4%
ValueCountFrequency (%)
0 5
 
0.1%
1 328
3.3%
2 225
2.2%
3 126
 
1.3%
4 120
 
1.2%
5 94
 
0.9%
6 80
 
0.8%
7 72
 
0.7%
8 77
 
0.8%
9 78
 
0.8%
ValueCountFrequency (%)
3828039 1
< 0.1%
3280652 1
< 0.1%
3245179 1
< 0.1%
3199480 1
< 0.1%
2990753 1
< 0.1%
2936133 1
< 0.1%
2108849 1
< 0.1%
1970983 1
< 0.1%
1887663 1
< 0.1%
1876471 1
< 0.1%

요양급여비용총액
Real number (ℝ)

HIGH CORRELATION 

Distinct9875
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5393207 × 109
Minimum0
Maximum2.58958 × 1011
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:36:12.432785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile98446.5
Q15625952.5
median69247355
Q35.4289784 × 108
95-th percentile6.1698241 × 109
Maximum2.58958 × 1011
Range2.58958 × 1011
Interquartile range (IQR)5.3727189 × 108

Descriptive statistics

Standard deviation7.6472046 × 109
Coefficient of variation (CV)4.9679085
Kurtosis306.8684
Mean1.5393207 × 109
Median Absolute Deviation (MAD)69002810
Skewness14.516677
Sum1.5393207 × 1013
Variance5.8479738 × 1019
MonotonicityNot monotonic
2023-12-13T00:36:12.621796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16970 40
 
0.4%
12130 12
 
0.1%
29100 8
 
0.1%
21180 7
 
0.1%
24260 6
 
0.1%
33940 4
 
< 0.1%
27910 4
 
< 0.1%
29260 4
 
< 0.1%
19350 4
 
< 0.1%
17950 4
 
< 0.1%
Other values (9865) 9907
99.1%
ValueCountFrequency (%)
0 2
< 0.1%
1760 1
< 0.1%
3290 1
< 0.1%
4930 1
< 0.1%
5100 2
< 0.1%
5610 2
< 0.1%
6020 1
< 0.1%
6070 1
< 0.1%
6630 1
< 0.1%
7000 1
< 0.1%
ValueCountFrequency (%)
258958000000 1
< 0.1%
209412000000 1
< 0.1%
173616000000 1
< 0.1%
163853000000 1
< 0.1%
143689000000 1
< 0.1%
130201000000 1
< 0.1%
118143000000 1
< 0.1%
116852000000 1
< 0.1%
116695000000 1
< 0.1%
116526000000 1
< 0.1%

보험자부담금
Real number (ℝ)

HIGH CORRELATION 

Distinct9873
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1834844 × 109
Minimum0
Maximum1.94692 × 1011
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:36:12.833994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile59845
Q13922810
median50328325
Q33.9175637 × 108
95-th percentile4.6368951 × 109
Maximum1.94692 × 1011
Range1.94692 × 1011
Interquartile range (IQR)3.8783356 × 108

Descriptive statistics

Standard deviation6.0931121 × 109
Coefficient of variation (CV)5.1484517
Kurtosis315.84884
Mean1.1834844 × 109
Median Absolute Deviation (MAD)50181470
Skewness14.81627
Sum1.1834844 × 1013
Variance3.7126015 × 1019
MonotonicityNot monotonic
2023-12-13T00:36:13.049066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11970 28
 
0.3%
15370 11
 
0.1%
10630 6
 
0.1%
20500 6
 
0.1%
8530 6
 
0.1%
3210 5
 
0.1%
3760 4
 
< 0.1%
14250 4
 
< 0.1%
14880 4
 
< 0.1%
17060 4
 
< 0.1%
Other values (9863) 9922
99.2%
ValueCountFrequency (%)
0 2
 
< 0.1%
1060 1
 
< 0.1%
2390 1
 
< 0.1%
2710 1
 
< 0.1%
2860 1
 
< 0.1%
2910 1
 
< 0.1%
3020 1
 
< 0.1%
3050 2
 
< 0.1%
3070 3
< 0.1%
3210 5
0.1%
ValueCountFrequency (%)
194692000000 1
< 0.1%
192135000000 1
< 0.1%
121855000000 1
< 0.1%
121338000000 1
< 0.1%
117465000000 1
< 0.1%
110272000000 1
< 0.1%
103475000000 1
< 0.1%
101436000000 1
< 0.1%
96339693560 1
< 0.1%
92020903370 1
< 0.1%

Interactions

2023-12-13T00:36:07.265507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:04.905014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:05.540915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:06.167869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:06.722192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:07.368557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:05.024179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:05.668894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:06.266419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:06.813137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:07.533026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:05.146726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:05.788432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:06.387678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:06.923962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:07.738137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:05.277710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:05.912371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:06.511478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:07.046248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:07.897874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:05.418195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:06.038837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:06.618863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:07.139685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:36:13.193602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별연령군환자수명세서청구건수입내원일수요양급여비용총액보험자부담금
성별1.0000.0000.0240.0170.0070.0000.020
연령군0.0001.0000.0400.0290.0430.0750.074
환자수0.0240.0401.0000.9200.8110.5990.563
명세서청구건수0.0170.0290.9201.0000.9680.7660.669
입내원일수0.0070.0430.8110.9681.0000.8410.637
요양급여비용총액0.0000.0750.5990.7660.8411.0000.946
보험자부담금0.0200.0740.5630.6690.6370.9461.000
2023-12-13T00:36:13.309620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별연령군
성별1.0000.000
연령군0.0001.000
2023-12-13T00:36:13.413455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
환자수명세서청구건수입내원일수요양급여비용총액보험자부담금성별연령군
환자수1.0000.9820.9650.8810.8660.0180.015
명세서청구건수0.9821.0000.9890.9230.9120.0130.011
입내원일수0.9650.9891.0000.9550.9470.0070.014
요양급여비용총액0.8810.9230.9551.0000.9990.0000.025
보험자부담금0.8660.9120.9470.9991.0000.0150.031
성별0.0180.0130.0070.0000.0151.0000.000
연령군0.0150.0110.0140.0250.0310.0001.000

Missing values

2023-12-13T00:36:08.450315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:36:08.687856image/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

진료년도주상병코드성별연령군환자수명세서청구건수입내원일수요양급여비용총액보험자부담금
179972022H36여17_80~84세19861527575279335629633802355456210
456652022T17남17_80~84세7128661023166577900124682310
562022A01여09_40~44세81111344940203940
201482022I26여12_55~59세50315762242640316940503420140
270782022L05남14_65~69세412102510352864351022340070
146842022G00여17_80~84세1750572221498620170989380
109252022E41남12_55~59세8415921799377607845400
342702022N89여08_35~39세7481018103710192697063994910
223272022J05남16_75~79세38471388511298512086712470
467822022T50여06_25~29세5697081091495555510382093320
진료년도주상병코드성별연령군환자수명세서청구건수입내원일수요양급여비용총액보험자부담금
464802022T41여09_40~44세255237730177230
404662022R62남03_10~14세8941408434095719441018701152865660
376872022Q72남07_30~34세55518589087890
392102022R22여15_70~74세387284808990824051580467510830
288612022L82남13_60~64세26844148416518487332090262850
261502022K66남18_85세 이상51116507249256330198305720
161982022G61남12_55~59세3142505720815412752201333963310
115202022E65남09_40~44세666315590204790
403332022R58남14_65~69세1732252401925170011945820
489852022Z13남08_35~39세14381949194810973625057821050