Overview

Dataset statistics

Number of variables14
Number of observations24
Missing cells1
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory129.5 B

Variable types

Text1
Categorical1
Numeric12

Dataset

Description장해등급별(1등급 ~ 11등급 이하) 연령별(30세 미만 ~ 80세 이상) 장해연금수급자 현황에 대한 데이터입니다. 30세 미만부터 시작됩니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15054103/fileData.do

Alerts

계 is highly overall correlated with 1등급 and 10 other fieldsHigh correlation
1등급 is highly overall correlated with 계 and 10 other fieldsHigh correlation
2등급 is highly overall correlated with 계 and 11 other fieldsHigh correlation
3등급 is highly overall correlated with 계 and 10 other fieldsHigh correlation
4등급 is highly overall correlated with 계 and 10 other fieldsHigh correlation
5등급 is highly overall correlated with 계 and 10 other fieldsHigh correlation
6등급 is highly overall correlated with 계 and 10 other fieldsHigh correlation
7등급 is highly overall correlated with 계 and 10 other fieldsHigh correlation
8등급 is highly overall correlated with 계 and 10 other fieldsHigh correlation
9등급 is highly overall correlated with 계 and 10 other fieldsHigh correlation
10등급 is highly overall correlated with 계 and 10 other fieldsHigh correlation
11등급이하 is highly overall correlated with 계 and 10 other fieldsHigh correlation
남여구분 is highly overall correlated with 2등급High correlation
5등급 has 1 (4.2%) missing valuesMissing
계 has 4 (16.7%) zerosZeros
1등급 has 9 (37.5%) zerosZeros
2등급 has 15 (62.5%) zerosZeros
3등급 has 11 (45.8%) zerosZeros
4등급 has 12 (50.0%) zerosZeros
5등급 has 10 (41.7%) zerosZeros
6등급 has 13 (54.2%) zerosZeros
7등급 has 11 (45.8%) zerosZeros
8등급 has 8 (33.3%) zerosZeros
9등급 has 12 (50.0%) zerosZeros
10등급 has 9 (37.5%) zerosZeros
11등급이하 has 6 (25.0%) zerosZeros

Reproduction

Analysis started2024-04-17 09:38:54.933090
Analysis finished2024-04-17 09:39:06.111286
Duration11.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

Distinct12
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-04-17T18:39:06.196632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters120
Distinct characters13
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

Unique0 ?
Unique (%)0.0%

Sample

1st row30세미만
2nd row30세미만
3rd row3034세
4th row3034세
5th row3539세
ValueCountFrequency (%)
30세미만 2
8.3%
3034세 2
8.3%
3539세 2
8.3%
4044세 2
8.3%
4549세 2
8.3%
5054세 2
8.3%
5559세 2
8.3%
6064세 2
8.3%
6569세 2
8.3%
7074세 2
8.3%
Other values (2) 4
16.7%
2024-04-17T18:39:06.440737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 24
20.0%
4 18
15.0%
5 18
15.0%
0 14
11.7%
3 10
8.3%
9 10
8.3%
6 8
 
6.7%
7 8
 
6.7%
미 2
 
1.7%
만 2
 
1.7%
Other values (3) 6
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 88
73.3%
Other Letter 32
 
26.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 18
20.5%
5 18
20.5%
0 14
15.9%
3 10
11.4%
9 10
11.4%
6 8
9.1%
7 8
9.1%
8 2
 
2.3%
Other Letter
ValueCountFrequency (%)
세 24
75.0%
미 2
 
6.2%
만 2
 
6.2%
이 2
 
6.2%
상 2
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 88
73.3%
Hangul 32
 
26.7%

Most frequent character per script

Common
ValueCountFrequency (%)
4 18
20.5%
5 18
20.5%
0 14
15.9%
3 10
11.4%
9 10
11.4%
6 8
9.1%
7 8
9.1%
8 2
 
2.3%
Hangul
ValueCountFrequency (%)
세 24
75.0%
미 2
 
6.2%
만 2
 
6.2%
이 2
 
6.2%
상 2
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88
73.3%
Hangul 32
 
26.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 24
75.0%
미 2
 
6.2%
만 2
 
6.2%
이 2
 
6.2%
상 2
 
6.2%
ASCII
ValueCountFrequency (%)
4 18
20.5%
5 18
20.5%
0 14
15.9%
3 10
11.4%
9 10
11.4%
6 8
9.1%
7 8
9.1%
8 2
 
2.3%

남여구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
남
12 
여
12 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남 12
50.0%
여 12
50.0%

Length

2024-04-17T18:39:06.550099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T18:39:06.627937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남 12
50.0%
여 12
50.0%

계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.91667
Minimum0
Maximum897
Zeros4
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-17T18:39:06.701993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.75
median18
Q3165.25
95-th percentile829.05
Maximum897
Range897
Interquartile range (IQR)163.5

Descriptive statistics

Standard deviation277.51442
Coefficient of variation (CV)1.7798894
Kurtosis2.6299853
Mean155.91667
Median Absolute Deviation (MAD)18
Skewness1.9452865
Sum3742
Variance77014.254
MonotonicityNot monotonic
2024-04-17T18:39:06.794164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 4
 
16.7%
1 2
 
8.3%
64 1
 
4.2%
8 1
 
4.2%
246 1
 
4.2%
12 1
 
4.2%
435 1
 
4.2%
26 1
 
4.2%
699 1
 
4.2%
34 1
 
4.2%
Other values (10) 10
41.7%
ValueCountFrequency (%)
0 4
16.7%
1 2
8.3%
2 1
 
4.2%
4 1
 
4.2%
7 1
 
4.2%
8 1
 
4.2%
12 1
 
4.2%
16 1
 
4.2%
20 1
 
4.2%
26 1
 
4.2%
ValueCountFrequency (%)
897 1
4.2%
852 1
4.2%
699 1
4.2%
435 1
4.2%
246 1
4.2%
241 1
4.2%
140 1
4.2%
64 1
4.2%
37 1
4.2%
34 1
4.2%

1등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7916667
Minimum0
Maximum26
Zeros9
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-17T18:39:06.879612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37.25
95-th percentile15.85
Maximum26
Range26
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation6.946874
Coefficient of variation (CV)1.4497824
Kurtosis2.4959629
Mean4.7916667
Median Absolute Deviation (MAD)1
Skewness1.6933557
Sum115
Variance48.259058
MonotonicityNot monotonic
2024-04-17T18:39:06.972442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 9
37.5%
1 4
16.7%
3 2
 
8.3%
5 1
 
4.2%
14 1
 
4.2%
12 1
 
4.2%
2 1
 
4.2%
26 1
 
4.2%
16 1
 
4.2%
15 1
 
4.2%
Other values (2) 2
 
8.3%
ValueCountFrequency (%)
0 9
37.5%
1 4
16.7%
2 1
 
4.2%
3 2
 
8.3%
5 1
 
4.2%
7 1
 
4.2%
8 1
 
4.2%
12 1
 
4.2%
14 1
 
4.2%
15 1
 
4.2%
ValueCountFrequency (%)
26 1
4.2%
16 1
4.2%
15 1
4.2%
14 1
4.2%
12 1
4.2%
8 1
4.2%
7 1
4.2%
5 1
4.2%
3 2
8.3%
2 1
4.2%

2등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1666667
Minimum0
Maximum19
Zeros15
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-17T18:39:07.061989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile13.85
Maximum19
Range19
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.4586721
Coefficient of variation (CV)1.7237912
Kurtosis2.2396284
Mean3.1666667
Median Absolute Deviation (MAD)0
Skewness1.7575873
Sum76
Variance29.797101
MonotonicityNot monotonic
2024-04-17T18:39:07.154385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 15
62.5%
5 2
 
8.3%
2 1
 
4.2%
9 1
 
4.2%
14 1
 
4.2%
19 1
 
4.2%
13 1
 
4.2%
1 1
 
4.2%
8 1
 
4.2%
ValueCountFrequency (%)
0 15
62.5%
1 1
 
4.2%
2 1
 
4.2%
5 2
 
8.3%
8 1
 
4.2%
9 1
 
4.2%
13 1
 
4.2%
14 1
 
4.2%
19 1
 
4.2%
ValueCountFrequency (%)
19 1
 
4.2%
14 1
 
4.2%
13 1
 
4.2%
9 1
 
4.2%
8 1
 
4.2%
5 2
 
8.3%
2 1
 
4.2%
1 1
 
4.2%
0 15
62.5%

3등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0416667
Minimum0
Maximum26
Zeros11
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-17T18:39:07.252468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36.25
95-th percentile22.7
Maximum26
Range26
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation7.969939
Coefficient of variation (CV)1.5808143
Kurtosis1.7967586
Mean5.0416667
Median Absolute Deviation (MAD)1
Skewness1.6980828
Sum121
Variance63.519928
MonotonicityNot monotonic
2024-04-17T18:39:07.346977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 11
45.8%
2 2
 
8.3%
1 2
 
8.3%
3 1
 
4.2%
5 1
 
4.2%
10 1
 
4.2%
26 1
 
4.2%
4 1
 
4.2%
23 1
 
4.2%
21 1
 
4.2%
Other values (2) 2
 
8.3%
ValueCountFrequency (%)
0 11
45.8%
1 2
 
8.3%
2 2
 
8.3%
3 1
 
4.2%
4 1
 
4.2%
5 1
 
4.2%
10 1
 
4.2%
11 1
 
4.2%
12 1
 
4.2%
21 1
 
4.2%
ValueCountFrequency (%)
26 1
4.2%
23 1
4.2%
21 1
4.2%
12 1
4.2%
11 1
4.2%
10 1
4.2%
5 1
4.2%
4 1
4.2%
3 1
4.2%
2 2
8.3%

4등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5
Minimum0
Maximum26
Zeros12
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-17T18:39:07.435260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q33.5
95-th percentile21.85
Maximum26
Range26
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation8.0595609
Coefficient of variation (CV)1.7910135
Kurtosis1.9180457
Mean4.5
Median Absolute Deviation (MAD)0.5
Skewness1.7900681
Sum108
Variance64.956522
MonotonicityNot monotonic
2024-04-17T18:39:07.532508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 12
50.0%
1 5
20.8%
2 1
 
4.2%
9 1
 
4.2%
22 1
 
4.2%
21 1
 
4.2%
26 1
 
4.2%
15 1
 
4.2%
8 1
 
4.2%
ValueCountFrequency (%)
0 12
50.0%
1 5
20.8%
2 1
 
4.2%
8 1
 
4.2%
9 1
 
4.2%
15 1
 
4.2%
21 1
 
4.2%
22 1
 
4.2%
26 1
 
4.2%
ValueCountFrequency (%)
26 1
 
4.2%
22 1
 
4.2%
21 1
 
4.2%
15 1
 
4.2%
9 1
 
4.2%
8 1
 
4.2%
2 1
 
4.2%
1 5
20.8%
0 12
50.0%

5등급
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct12
Distinct (%)52.2%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean9.826087
Minimum0
Maximum49
Zeros10
Zeros (%)41.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-17T18:39:07.623582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q312.5
95-th percentile43.3
Maximum49
Range49
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation15.752784
Coefficient of variation (CV)1.6031594
Kurtosis1.1366983
Mean9.826087
Median Absolute Deviation (MAD)1
Skewness1.5747938
Sum226
Variance248.1502
MonotonicityNot monotonic
2024-04-17T18:39:07.716146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 10
41.7%
1 2
 
8.3%
3 2
 
8.3%
11 1
 
4.2%
14 1
 
4.2%
6 1
 
4.2%
37 1
 
4.2%
2 1
 
4.2%
49 1
 
4.2%
44 1
 
4.2%
Other values (2) 2
 
8.3%
ValueCountFrequency (%)
0 10
41.7%
1 2
 
8.3%
2 1
 
4.2%
3 2
 
8.3%
6 1
 
4.2%
11 1
 
4.2%
14 1
 
4.2%
21 1
 
4.2%
34 1
 
4.2%
37 1
 
4.2%
ValueCountFrequency (%)
49 1
4.2%
44 1
4.2%
37 1
4.2%
34 1
4.2%
21 1
4.2%
14 1
4.2%
11 1
4.2%
6 1
4.2%
3 2
8.3%
2 1
4.2%

6등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0833333
Minimum0
Maximum21
Zeros13
Zeros (%)54.2%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-17T18:39:07.824169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36.25
95-th percentile19.1
Maximum21
Range21
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation6.5402876
Coefficient of variation (CV)1.6017031
Kurtosis1.6323822
Mean4.0833333
Median Absolute Deviation (MAD)0
Skewness1.6269031
Sum98
Variance42.775362
MonotonicityNot monotonic
2024-04-17T18:39:07.922183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 13
54.2%
1 2
 
8.3%
3 2
 
8.3%
11 2
 
8.3%
6 1
 
4.2%
7 1
 
4.2%
21 1
 
4.2%
20 1
 
4.2%
14 1
 
4.2%
ValueCountFrequency (%)
0 13
54.2%
1 2
 
8.3%
3 2
 
8.3%
6 1
 
4.2%
7 1
 
4.2%
11 2
 
8.3%
14 1
 
4.2%
20 1
 
4.2%
21 1
 
4.2%
ValueCountFrequency (%)
21 1
 
4.2%
20 1
 
4.2%
14 1
 
4.2%
11 2
 
8.3%
7 1
 
4.2%
6 1
 
4.2%
3 2
 
8.3%
1 2
 
8.3%
0 13
54.2%

7등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.333333
Minimum0
Maximum69
Zeros11
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-17T18:39:08.006404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q310.25
95-th percentile55.55
Maximum69
Range69
Interquartile range (IQR)10.25

Descriptive statistics

Standard deviation21.2637
Coefficient of variation (CV)1.7240838
Kurtosis1.7076514
Mean12.333333
Median Absolute Deviation (MAD)1
Skewness1.7435428
Sum296
Variance452.14493
MonotonicityNot monotonic
2024-04-17T18:39:08.097931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 11
45.8%
1 2
 
8.3%
7 2
 
8.3%
6 1
 
4.2%
2 1
 
4.2%
20 1
 
4.2%
56 1
 
4.2%
69 1
 
4.2%
53 1
 
4.2%
4 1
 
4.2%
Other values (2) 2
 
8.3%
ValueCountFrequency (%)
0 11
45.8%
1 2
 
8.3%
2 1
 
4.2%
4 1
 
4.2%
6 1
 
4.2%
7 2
 
8.3%
20 1
 
4.2%
22 1
 
4.2%
48 1
 
4.2%
53 1
 
4.2%
ValueCountFrequency (%)
69 1
4.2%
56 1
4.2%
53 1
4.2%
48 1
4.2%
22 1
4.2%
20 1
4.2%
7 2
8.3%
6 1
4.2%
4 1
4.2%
2 1
4.2%

8등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.666667
Minimum0
Maximum152
Zeros8
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-17T18:39:08.190100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q323.25
95-th percentile136.7
Maximum152
Range152
Interquartile range (IQR)23.25

Descriptive statistics

Standard deviation45.949563
Coefficient of variation (CV)1.8628201
Kurtosis3.1780241
Mean24.666667
Median Absolute Deviation (MAD)1.5
Skewness2.0749456
Sum592
Variance2111.3623
MonotonicityNot monotonic
2024-04-17T18:39:08.280185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 8
33.3%
1 4
16.7%
5 2
 
8.3%
4 1
 
4.2%
20 1
 
4.2%
36 1
 
4.2%
2 1
 
4.2%
152 1
 
4.2%
14 1
 
4.2%
140 1
 
4.2%
Other values (3) 3
 
12.5%
ValueCountFrequency (%)
0 8
33.3%
1 4
16.7%
2 1
 
4.2%
4 1
 
4.2%
5 2
 
8.3%
14 1
 
4.2%
20 1
 
4.2%
33 1
 
4.2%
36 1
 
4.2%
59 1
 
4.2%
ValueCountFrequency (%)
152 1
4.2%
140 1
4.2%
118 1
4.2%
59 1
4.2%
36 1
4.2%
33 1
4.2%
20 1
4.2%
14 1
4.2%
5 2
8.3%
4 1
4.2%

9등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.5
Minimum0
Maximum88
Zeros12
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-17T18:39:08.373850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q311.75
95-th percentile68.5
Maximum88
Range88
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation25.061663
Coefficient of variation (CV)1.8564195
Kurtosis3.2058986
Mean13.5
Median Absolute Deviation (MAD)0.5
Skewness2.0234385
Sum324
Variance628.08696
MonotonicityNot monotonic
2024-04-17T18:39:08.459898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 12
50.0%
3 2
 
8.3%
9 1
 
4.2%
20 1
 
4.2%
60 1
 
4.2%
88 1
 
4.2%
2 1
 
4.2%
70 1
 
4.2%
4 1
 
4.2%
34 1
 
4.2%
Other values (2) 2
 
8.3%
ValueCountFrequency (%)
0 12
50.0%
1 1
 
4.2%
2 1
 
4.2%
3 2
 
8.3%
4 1
 
4.2%
9 1
 
4.2%
20 1
 
4.2%
30 1
 
4.2%
34 1
 
4.2%
60 1
 
4.2%
ValueCountFrequency (%)
88 1
4.2%
70 1
4.2%
60 1
4.2%
34 1
4.2%
30 1
4.2%
20 1
4.2%
9 1
4.2%
4 1
4.2%
3 2
8.3%
2 1
4.2%

10등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.458333
Minimum0
Maximum114
Zeros9
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-17T18:39:08.549969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q310
95-th percentile87.65
Maximum114
Range114
Interquartile range (IQR)10

Descriptive statistics

Standard deviation31.058121
Coefficient of variation (CV)2.0091507
Kurtosis4.7509149
Mean15.458333
Median Absolute Deviation (MAD)1
Skewness2.3326315
Sum371
Variance964.60688
MonotonicityNot monotonic
2024-04-17T18:39:08.641730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 9
37.5%
1 4
16.7%
2 3
 
12.5%
7 1
 
4.2%
21 1
 
4.2%
92 1
 
4.2%
5 1
 
4.2%
114 1
 
4.2%
63 1
 
4.2%
40 1
 
4.2%
ValueCountFrequency (%)
0 9
37.5%
1 4
16.7%
2 3
 
12.5%
5 1
 
4.2%
7 1
 
4.2%
19 1
 
4.2%
21 1
 
4.2%
40 1
 
4.2%
63 1
 
4.2%
92 1
 
4.2%
ValueCountFrequency (%)
114 1
 
4.2%
92 1
 
4.2%
63 1
 
4.2%
40 1
 
4.2%
21 1
 
4.2%
19 1
 
4.2%
7 1
 
4.2%
5 1
 
4.2%
2 3
12.5%
1 4
16.7%

11등급이하
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.958333
Minimum0
Maximum346
Zeros6
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-17T18:39:08.729578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median7
Q360.5
95-th percentile326.6
Maximum346
Range346
Interquartile range (IQR)59.75

Descriptive statistics

Standard deviation107.23784
Coefficient of variation (CV)1.8188751
Kurtosis2.9048578
Mean58.958333
Median Absolute Deviation (MAD)7
Skewness2.0106623
Sum1415
Variance11499.955
MonotonicityNot monotonic
2024-04-17T18:39:08.817414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 6
25.0%
7 3
12.5%
2 2
 
8.3%
16 2
 
8.3%
1 2
 
8.3%
55 1
 
4.2%
83 1
 
4.2%
346 1
 
4.2%
24 1
 
4.2%
338 1
 
4.2%
Other values (4) 4
16.7%
ValueCountFrequency (%)
0 6
25.0%
1 2
 
8.3%
2 2
 
8.3%
3 1
 
4.2%
7 3
12.5%
16 2
 
8.3%
24 1
 
4.2%
55 1
 
4.2%
77 1
 
4.2%
83 1
 
4.2%
ValueCountFrequency (%)
346 1
 
4.2%
338 1
 
4.2%
262 1
 
4.2%
168 1
 
4.2%
83 1
 
4.2%
77 1
 
4.2%
55 1
 
4.2%
24 1
 
4.2%
16 2
8.3%
7 3
12.5%

Interactions

2024-04-17T18:39:04.747281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:55.294834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:56.035595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:57.095173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:57.960483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:58.739254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:59.521614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:00.348011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:01.193766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:02.261059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:03.140318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:03.952306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:04.809208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:55.348887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:56.095530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:57.157837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:58.020497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:58.797891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:59.584116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:00.429076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:01.258949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:02.333645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:03.198350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:04.014312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:04.879778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:55.405452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:56.159894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:57.221955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:58.081429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:58.859249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:59.649446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:00.500876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:01.321570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:02.411913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:03.268703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:04.080632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:04.951580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:55.469768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:56.230259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:57.295696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:58.154197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:58.933385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:59.719526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:00.570279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:01.637029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:02.504254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:03.340724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:04.150108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:05.022121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:55.527642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:56.298025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:57.367730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:58.219361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:59.004210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:59.784536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:00.630065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:01.700238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:02.574163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:03.406700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:04.227068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:05.095056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:55.586319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:56.373008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:57.436602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:58.284783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:59.065902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:59.846346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:00.695478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:01.768023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:02.641032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:03.486026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:04.290159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:05.170363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:55.644557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:56.435415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:57.517851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:58.346585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:59.126202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:59.914308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:00.757176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:01.831764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:02.713728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:03.561123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:04.349841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:05.234025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:55.701114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:56.496040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:57.594667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:58.410025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:59.188372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:59.977816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:00.819626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:01.895622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:02.777247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:03.622088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:04.411452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:05.319273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:55.766804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:56.563640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:57.684806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:58.478968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:59.255865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:00.050292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:00.905890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:01.965978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:02.851748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:03.688938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:04.481055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:05.390410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:55.835876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:56.633769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:57.757838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:58.549495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:59.324401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:00.120695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:00.988557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:02.037701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:02.921175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:03.757965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:04.550704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:05.454190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:55.907048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:56.702532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:57.821631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:58.607677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:59.382740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:00.182426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:01.063457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:02.104245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:02.992910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:03.815826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:04.609820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:05.525522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:55.968467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:56.766591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:57.887154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:58.669866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:38:59.453313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:00.252622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:01.129545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:02.174163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:03.062002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:03.879934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:39:04.674414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T18:39:08.899628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분남여구분계1등급2등급3등급4등급5등급6등급7등급8등급9등급10등급11등급이하
구분1.0000.0000.0770.0940.0000.0000.0000.0000.3170.4520.0770.2360.3050.077
남여구분0.0001.0000.6900.7030.5370.6720.3670.5390.4750.4560.6900.4330.5490.690
계0.0770.6901.0000.9360.8880.9481.0000.9540.9470.9361.0000.9470.9921.000
1등급0.0940.7030.9361.0000.9240.9310.9380.9850.8920.9900.9360.9510.9710.936
2등급0.0000.5370.8880.9241.0000.8840.8800.9200.9530.9200.8880.9860.9280.888
3등급0.0000.6720.9480.9310.8841.0000.8260.9020.9040.9430.9480.9500.9710.948
4등급0.0000.3671.0000.9380.8800.8261.0000.9610.9111.0001.0000.9111.0001.000
5등급0.0000.5390.9540.9850.9200.9020.9611.0000.8810.9890.9540.9500.9900.954
6등급0.3170.4750.9470.8920.9530.9040.9110.8811.0000.9000.9470.9950.8630.947
7등급0.4520.4560.9360.9900.9200.9431.0000.9890.9001.0000.9360.9511.0000.936
8등급0.0770.6901.0000.9360.8880.9481.0000.9540.9470.9361.0000.9470.9921.000
9등급0.2360.4330.9470.9510.9860.9500.9110.9500.9950.9510.9471.0000.9470.947
10등급0.3050.5490.9920.9710.9280.9711.0000.9900.8631.0000.9920.9471.0000.992
11등급이하0.0770.6901.0000.9360.8880.9481.0000.9540.9470.9361.0000.9470.9921.000
2024-04-17T18:39:09.020450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계1등급2등급3등급4등급5등급6등급7등급8등급9등급10등급11등급이하남여구분
계1.0000.8530.8430.9440.8570.8820.8680.8690.9720.8710.9540.9780.451
1등급0.8531.0000.8520.8750.8820.8400.8280.7350.7980.6970.8230.8060.448
2등급0.8430.8521.0000.8700.9010.8150.8400.7760.8540.8320.8680.8380.500
3등급0.9440.8750.8701.0000.8280.9480.8980.8570.9420.8080.9490.9060.437
4등급0.8570.8820.9010.8281.0000.7470.8110.8020.8390.8510.8620.8650.408
5등급0.8820.8400.8150.9480.7471.0000.8740.7750.8600.7190.8710.8310.325
6등급0.8680.8280.8400.8980.8110.8741.0000.8000.8520.8180.8340.8430.438
7등급0.8690.7350.7760.8570.8020.7750.8001.0000.8780.8550.8940.8370.272
8등급0.9720.7980.8540.9420.8390.8600.8520.8781.0000.9020.9810.9600.451
9등급0.8710.6970.8320.8080.8510.7190.8180.8550.9021.0000.8620.8910.397
10등급0.9540.8230.8680.9490.8620.8710.8340.8940.9810.8621.0000.9280.348
11등급이하0.9780.8060.8380.9060.8650.8310.8430.8370.9600.8910.9281.0000.451
남여구분0.4510.4480.5000.4370.4080.3250.4380.2720.4510.3970.3480.4511.000

Missing values

2024-04-17T18:39:05.634813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T18:39:06.057785image/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

구분남여구분계1등급2등급3등급4등급5등급6등급7등급8등급9등급10등급11등급이하
030세미만남000000000000
130세미만여00000<NA>000000
23034세남000000000000
33034세여110000000000
43539세남200000000002
53539세여100001000000
64044세남730010010002
74044세여000000000000
84549세남37523131040216
94549세여400000011011
구분남여구분계1등급2등급3등급4등급5등급6등급7등급8등급9등급10등급11등급이하
146064세남852261426223721561526092346
156064세여641041237143524
166569세남8971619232149206914088114338
176569세여34001010752216
187074세남699151321264414531187063262
197074세여2611110045427
207579세남435781115341148593440168
217579세여1200000101307
2280세이상남2468512821112233301977
2380세이상여810010001113