Overview

Dataset statistics

Number of variables19
Number of observations33
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 KiB
Average record size in memory174.0 B

Variable types

Text1
Numeric9
Categorical9

Dataset

Description유족유형별(배우자, 부모, 자녀, 손자녀 등) 연령별(18세 미만 ~ 65세 이상) 유족연금수급자 현황에 대한 데이터입니다. 18세 미만부터 시작됩니다.
URLhttps://www.data.go.kr/data/15054102/fileData.do

Alerts

조부모(계) has constant value ""Constant
조부모(남) has constant value ""Constant
조부모(여) has constant value ""Constant
부모(계) is highly overall correlated with 계 and 10 other fieldsHigh correlation
손자녀(계) is highly overall correlated with 손자녀(남) and 1 other fieldsHigh correlation
손자녀(남) is highly overall correlated with 손자녀(계)High correlation
손자녀(여) is highly overall correlated with 손자녀(계)High correlation
부모(여) is highly overall correlated with 계 and 10 other fieldsHigh correlation
계 is highly overall correlated with 남 and 10 other fieldsHigh correlation
남 is highly overall correlated with 계 and 10 other fieldsHigh correlation
여 is highly overall correlated with 계 and 10 other fieldsHigh correlation
배우자(계) is highly overall correlated with 계 and 10 other fieldsHigh correlation
배우자(남) is highly overall correlated with 계 and 10 other fieldsHigh correlation
배우자(여) is highly overall correlated with 계 and 10 other fieldsHigh correlation
자녀(계) is highly overall correlated with 계 and 10 other fieldsHigh correlation
자녀(남) is highly overall correlated with 계 and 10 other fieldsHigh correlation
자녀(여) is highly overall correlated with 계 and 10 other fieldsHigh correlation
부모(남) is highly overall correlated with 계 and 10 other fieldsHigh correlation
부모(계) is highly imbalanced (56.5%)Imbalance
부모(남) is highly imbalanced (67.0%)Imbalance
부모(여) is highly imbalanced (57.8%)Imbalance
손자녀(계) is highly imbalanced (75.4%)Imbalance
손자녀(남) is highly imbalanced (67.0%)Imbalance
손자녀(여) is highly imbalanced (80.4%)Imbalance
구분 has unique valuesUnique
배우자(계) has 2 (6.1%) zerosZeros
배우자(남) has 3 (9.1%) zerosZeros
배우자(여) has 2 (6.1%) zerosZeros

Reproduction

Analysis started2023-12-12 14:27:53.142594
Analysis finished2023-12-12 14:28:03.095184
Duration9.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-12T23:28:03.240747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.3030303
Min length3

Characters and Unicode

Total characters109
Distinct characters15
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

Unique33 ?
Unique (%)100.0%

Sample

1st row18세미만
2nd row19세미만
3rd row36세미만
4th row36세이상
5th row37세
ValueCountFrequency (%)
18세미만 1
 
3.0%
50세 1
 
3.0%
64세 1
 
3.0%
63세 1
 
3.0%
62세 1
 
3.0%
61세 1
 
3.0%
60세 1
 
3.0%
59세 1
 
3.0%
58세 1
 
3.0%
57세 1
 
3.0%
Other values (23) 23
69.7%
2023-12-12T23:28:03.597520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 33
30.3%
4 13
 
11.9%
5 13
 
11.9%
6 10
 
9.2%
3 8
 
7.3%
1 5
 
4.6%
8 4
 
3.7%
9 4
 
3.7%
미 3
 
2.8%
만 3
 
2.8%
Other values (5) 13
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66
60.6%
Other Letter 43
39.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 13
19.7%
5 13
19.7%
6 10
15.2%
3 8
12.1%
1 5
 
7.6%
8 4
 
6.1%
9 4
 
6.1%
7 3
 
4.5%
0 3
 
4.5%
2 3
 
4.5%
Other Letter
ValueCountFrequency (%)
세 33
76.7%
미 3
 
7.0%
만 3
 
7.0%
이 2
 
4.7%
상 2
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 66
60.6%
Hangul 43
39.4%

Most frequent character per script

Common
ValueCountFrequency (%)
4 13
19.7%
5 13
19.7%
6 10
15.2%
3 8
12.1%
1 5
 
7.6%
8 4
 
6.1%
9 4
 
6.1%
7 3
 
4.5%
0 3
 
4.5%
2 3
 
4.5%
Hangul
ValueCountFrequency (%)
세 33
76.7%
미 3
 
7.0%
만 3
 
7.0%
이 2
 
4.7%
상 2
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66
60.6%
Hangul 43
39.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 33
76.7%
미 3
 
7.0%
만 3
 
7.0%
이 2
 
4.7%
상 2
 
4.7%
ASCII
ValueCountFrequency (%)
4 13
19.7%
5 13
19.7%
6 10
15.2%
3 8
12.1%
1 5
 
7.6%
8 4
 
6.1%
9 4
 
6.1%
7 3
 
4.5%
0 3
 
4.5%
2 3
 
4.5%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2226.0909
Minimum6
Maximum60716
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T23:28:03.724232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11.2
Q146
median166
Q3577
95-th percentile1488
Maximum60716
Range60710
Interquartile range (IQR)531

Descriptive statistics

Standard deviation10511.089
Coefficient of variation (CV)4.7217701
Kurtosis32.846961
Mean2226.0909
Median Absolute Deviation (MAD)152
Skewness5.7253053
Sum73461
Variance1.10483 × 108
MonotonicityNot monotonic
2023-12-12T23:28:03.841934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
28 2
 
6.1%
229 1
 
3.0%
60716 1
 
3.0%
1593 1
 
3.0%
1418 1
 
3.0%
1403 1
 
3.0%
1400 1
 
3.0%
1143 1
 
3.0%
936 1
 
3.0%
651 1
 
3.0%
Other values (22) 22
66.7%
ValueCountFrequency (%)
6 1
3.0%
7 1
3.0%
14 1
3.0%
18 1
3.0%
28 2
6.1%
32 1
3.0%
44 1
3.0%
46 1
3.0%
55 1
3.0%
59 1
3.0%
ValueCountFrequency (%)
60716 1
3.0%
1593 1
3.0%
1418 1
3.0%
1403 1
3.0%
1400 1
3.0%
1143 1
3.0%
936 1
3.0%
651 1
3.0%
577 1
3.0%
530 1
3.0%

남
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.575758
Minimum1
Maximum1582
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T23:28:03.957006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.4
Q117
median33
Q366
95-th percentile119.2
Maximum1582
Range1581
Interquartile range (IQR)49

Descriptive statistics

Standard deviation270.11329
Coefficient of variation (CV)2.9821809
Kurtosis31.742292
Mean90.575758
Median Absolute Deviation (MAD)23
Skewness5.5866968
Sum2989
Variance72961.189
MonotonicityNot monotonic
2023-12-12T23:28:04.081677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
8 2
 
6.1%
103 2
 
6.1%
66 2
 
6.1%
56 2
 
6.1%
19 1
 
3.0%
33 1
 
3.0%
1582 1
 
3.0%
127 1
 
3.0%
109 1
 
3.0%
114 1
 
3.0%
Other values (19) 19
57.6%
ValueCountFrequency (%)
1 1
3.0%
4 1
3.0%
8 2
6.1%
10 1
3.0%
11 1
3.0%
12 1
3.0%
13 1
3.0%
17 1
3.0%
18 1
3.0%
19 1
3.0%
ValueCountFrequency (%)
1582 1
3.0%
127 1
3.0%
114 1
3.0%
109 1
3.0%
103 2
6.1%
82 1
3.0%
69 1
3.0%
66 2
6.1%
56 2
6.1%
54 1
3.0%

여
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2135.5152
Minimum2
Maximum59134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T23:28:04.200684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q126
median128
Q3521
95-th percentile1371.8
Maximum59134
Range59132
Interquartile range (IQR)495

Descriptive statistics

Standard deviation10242.067
Coefficient of variation (CV)4.7960641
Kurtosis32.861069
Mean2135.5152
Median Absolute Deviation (MAD)122
Skewness5.727078
Sum70472
Variance1.0489995 × 108
MonotonicityNot monotonic
2023-12-12T23:28:04.347022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
6 2
 
6.1%
9 1
 
3.0%
854 1
 
3.0%
347 1
 
3.0%
360 1
 
3.0%
393 1
 
3.0%
461 1
 
3.0%
521 1
 
3.0%
585 1
 
3.0%
1040 1
 
3.0%
Other values (22) 22
66.7%
ValueCountFrequency (%)
2 1
3.0%
6 2
6.1%
9 1
3.0%
10 1
3.0%
18 1
3.0%
21 1
3.0%
23 1
3.0%
26 1
3.0%
38 1
3.0%
46 1
3.0%
ValueCountFrequency (%)
59134 1
3.0%
1466 1
3.0%
1309 1
3.0%
1300 1
3.0%
1286 1
3.0%
1040 1
3.0%
854 1
3.0%
585 1
3.0%
521 1
3.0%
461 1
3.0%

배우자(계)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2315.6364
Minimum0
Maximum64993
Zeros2
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T23:28:04.490609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.6
Q128
median148
Q3532
95-th percentile1517
Maximum64993
Range64993
Interquartile range (IQR)504

Descriptive statistics

Standard deviation11261.404
Coefficient of variation (CV)4.8632003
Kurtosis32.875197
Mean2315.6364
Median Absolute Deviation (MAD)141
Skewness5.7288631
Sum76416
Variance1.2681921 × 108
MonotonicityNot monotonic
2023-12-12T23:28:04.632826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 2
 
6.1%
20 2
 
6.1%
232 1
 
3.0%
64993 1
 
3.0%
1532 1
 
3.0%
1507 1
 
3.0%
1469 1
 
3.0%
1186 1
 
3.0%
979 1
 
3.0%
674 1
 
3.0%
Other values (21) 21
63.6%
ValueCountFrequency (%)
0 2
6.1%
6 1
3.0%
7 1
3.0%
8 1
3.0%
9 1
3.0%
20 2
6.1%
28 1
3.0%
37 1
3.0%
49 1
3.0%
57 1
3.0%
ValueCountFrequency (%)
64993 1
3.0%
1532 1
3.0%
1507 1
3.0%
1469 1
3.0%
1186 1
3.0%
979 1
3.0%
674 1
3.0%
611 1
3.0%
532 1
3.0%
437 1
3.0%

배우자(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.848485
Minimum0
Maximum1586
Zeros3
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T23:28:04.769742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median17
Q343
95-th percentile87.8
Maximum1586
Range1586
Interquartile range (IQR)38

Descriptive statistics

Standard deviation273.0094
Coefficient of variation (CV)3.7476332
Kurtosis32.277246
Mean72.848485
Median Absolute Deviation (MAD)13
Skewness5.6540841
Sum2404
Variance74534.133
MonotonicityNot monotonic
2023-12-12T23:28:04.939512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 3
 
9.1%
1 3
 
9.1%
5 2
 
6.1%
7 2
 
6.1%
11 2
 
6.1%
25 1
 
3.0%
1586 1
 
3.0%
92 1
 
3.0%
82 1
 
3.0%
85 1
 
3.0%
Other values (16) 16
48.5%
ValueCountFrequency (%)
0 3
9.1%
1 3
9.1%
4 1
 
3.0%
5 2
6.1%
7 2
6.1%
9 1
 
3.0%
10 1
 
3.0%
11 2
6.1%
13 1
 
3.0%
17 1
 
3.0%
ValueCountFrequency (%)
1586 1
3.0%
92 1
3.0%
85 1
3.0%
82 1
3.0%
73 1
3.0%
72 1
3.0%
48 1
3.0%
45 1
3.0%
43 1
3.0%
36 1
3.0%

배우자(여)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2242.7879
Minimum0
Maximum63407
Zeros2
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T23:28:05.093106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123
median131
Q3487
95-th percentile1431
Maximum63407
Range63407
Interquartile range (IQR)464

Descriptive statistics

Standard deviation10988.896
Coefficient of variation (CV)4.8996589
Kurtosis32.883554
Mean2242.7879
Median Absolute Deviation (MAD)125
Skewness5.7299124
Sum74012
Variance1.2075583 × 108
MonotonicityNot monotonic
2023-12-12T23:28:05.218708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 2
 
6.1%
201 1
 
3.0%
63407 1
 
3.0%
1440 1
 
3.0%
1425 1
 
3.0%
1384 1
 
3.0%
1114 1
 
3.0%
906 1
 
3.0%
626 1
 
3.0%
568 1
 
3.0%
Other values (22) 22
66.7%
ValueCountFrequency (%)
0 2
6.1%
5 1
3.0%
6 1
3.0%
7 1
3.0%
9 1
3.0%
15 1
3.0%
16 1
3.0%
23 1
3.0%
30 1
3.0%
42 1
3.0%
ValueCountFrequency (%)
63407 1
3.0%
1440 1
3.0%
1425 1
3.0%
1384 1
3.0%
1114 1
3.0%
906 1
3.0%
626 1
3.0%
568 1
3.0%
487 1
3.0%
412 1
3.0%

부모(계)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size396.0 B
0
27 
1
3 
5
 
1
6
 
1
756
 
1

Length

Max length3
Median length1
Mean length1.0606061
Min length1

Unique

Unique3 ?
Unique (%)9.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 27
81.8%
1 3
 
9.1%
5 1
 
3.0%
6 1
 
3.0%
756 1
 
3.0%

Length

2023-12-12T23:28:05.408371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:28:05.549749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
81.8%
1 3
 
9.1%
5 1
 
3.0%
6 1
 
3.0%
756 1
 
3.0%

부모(남)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
0
30 
1
 
2
101
 
1

Length

Max length3
Median length1
Mean length1.0606061
Min length1

Unique

Unique1 ?
Unique (%)3.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30
90.9%
1 2
 
6.1%
101 1
 
3.0%

Length

2023-12-12T23:28:05.715837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:28:05.857160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
90.9%
1 2
 
6.1%
101 1
 
3.0%

부모(여)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
0
28 
1
 
2
5
 
2
655
 
1

Length

Max length3
Median length1
Mean length1.0606061
Min length1

Unique

Unique1 ?
Unique (%)3.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28
84.8%
1 2
 
6.1%
5 2
 
6.1%
655 1
 
3.0%

Length

2023-12-12T23:28:05.998911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:28:06.128163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
84.8%
1 2
 
6.1%
5 2
 
6.1%
655 1
 
3.0%

자녀(계)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.666667
Minimum2
Maximum254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T23:28:06.232465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.8
Q117
median39
Q349
95-th percentile64.8
Maximum254
Range252
Interquartile range (IQR)32

Descriptive statistics

Standard deviation43.126751
Coefficient of variation (CV)1.087229
Kurtosis19.821628
Mean39.666667
Median Absolute Deviation (MAD)19
Skewness3.9768801
Sum1309
Variance1859.9167
MonotonicityNot monotonic
2023-12-12T23:28:06.377068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
11 3
 
9.1%
39 2
 
6.1%
17 2
 
6.1%
49 2
 
6.1%
58 2
 
6.1%
46 1
 
3.0%
43 1
 
3.0%
254 1
 
3.0%
47 1
 
3.0%
59 1
 
3.0%
Other values (17) 17
51.5%
ValueCountFrequency (%)
2 1
 
3.0%
3 1
 
3.0%
6 1
 
3.0%
8 1
 
3.0%
9 1
 
3.0%
11 3
9.1%
17 2
6.1%
18 1
 
3.0%
20 1
 
3.0%
23 1
 
3.0%
ValueCountFrequency (%)
254 1
3.0%
69 1
3.0%
62 1
3.0%
59 1
3.0%
58 2
6.1%
55 1
3.0%
49 2
6.1%
47 1
3.0%
46 1
3.0%
45 1
3.0%

자녀(남)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.363636
Minimum1
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T23:28:06.511169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.2
Q19
median23
Q331
95-th percentile42.2
Maximum160
Range159
Interquartile range (IQR)22

Descriptive statistics

Standard deviation27.137173
Coefficient of variation (CV)1.0699244
Kurtosis19.66811
Mean25.363636
Median Absolute Deviation (MAD)12
Skewness3.9600812
Sum837
Variance736.42614
MonotonicityNot monotonic
2023-12-12T23:28:06.679247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
8 3
 
9.1%
23 2
 
6.1%
6 2
 
6.1%
17 2
 
6.1%
9 2
 
6.1%
26 2
 
6.1%
30 2
 
6.1%
29 1
 
3.0%
39 1
 
3.0%
160 1
 
3.0%
Other values (15) 15
45.5%
ValueCountFrequency (%)
1 1
 
3.0%
2 1
 
3.0%
4 1
 
3.0%
6 2
6.1%
8 3
9.1%
9 2
6.1%
14 1
 
3.0%
17 2
6.1%
19 1
 
3.0%
20 1
 
3.0%
ValueCountFrequency (%)
160 1
3.0%
44 1
3.0%
41 1
3.0%
39 1
3.0%
37 1
3.0%
36 1
3.0%
35 1
3.0%
34 1
3.0%
31 1
3.0%
30 2
6.1%

자녀(여)
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)57.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.30303
Minimum1
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T23:28:06.806238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median12
Q318
95-th percentile24.4
Maximum94
Range93
Interquartile range (IQR)15

Descriptive statistics

Standard deviation16.346798
Coefficient of variation (CV)1.1428905
Kurtosis18.138102
Mean14.30303
Median Absolute Deviation (MAD)7
Skewness3.7426679
Sum472
Variance267.2178
MonotonicityNot monotonic
2023-12-12T23:28:06.917217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 4
12.1%
18 4
12.1%
1 3
 
9.1%
17 2
 
6.1%
23 2
 
6.1%
9 2
 
6.1%
12 2
 
6.1%
2 2
 
6.1%
19 2
 
6.1%
94 1
 
3.0%
Other values (9) 9
27.3%
ValueCountFrequency (%)
1 3
9.1%
2 2
6.1%
3 4
12.1%
4 1
 
3.0%
5 1
 
3.0%
8 1
 
3.0%
9 2
6.1%
10 1
 
3.0%
12 2
6.1%
13 1
 
3.0%
ValueCountFrequency (%)
94 1
 
3.0%
25 1
 
3.0%
24 1
 
3.0%
23 2
6.1%
22 1
 
3.0%
19 2
6.1%
18 4
12.1%
17 2
6.1%
16 1
 
3.0%
13 1
 
3.0%

손자녀(계)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
0
31 
2
 
1
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)6.1%

Sample

1st row0
2nd row2
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 31
93.9%
2 1
 
3.0%
1 1
 
3.0%

Length

2023-12-12T23:28:07.046443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:28:07.152998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31
93.9%
2 1
 
3.0%
1 1
 
3.0%

손자녀(남)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
0
31 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 31
93.9%
1 2
 
6.1%

Length

2023-12-12T23:28:07.269777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:28:07.369079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31
93.9%
1 2
 
6.1%

손자녀(여)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
0
32 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 32
97.0%
1 1
 
3.0%

Length

2023-12-12T23:28:07.475785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:28:07.584381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 32
97.0%
1 1
 
3.0%

조부모(계)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
0
33 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 33
100.0%

Length

2023-12-12T23:28:07.703897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:28:07.800115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 33
100.0%

조부모(남)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
0
33 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 33
100.0%

Length

2023-12-12T23:28:07.884994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:28:07.973730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 33
100.0%

조부모(여)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
0
33 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 33
100.0%

Length

2023-12-12T23:28:08.066635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:28:08.150309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 33
100.0%

Interactions

2023-12-12T23:28:01.605127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:53.915476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:54.748762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:55.631488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:56.902853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:57.888445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:58.896994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:59.792167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:00.660127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:01.702199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:54.015876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:54.838209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:56.041563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:57.017210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:58.007776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:59.005817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:59.895387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:00.747941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:01.787334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:54.120781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:54.966661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:56.133485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:57.160105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:58.146053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:59.111767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:59.994918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:00.861946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:01.865983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:54.199250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:55.051993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:56.289481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:57.266903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:58.266663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:59.208070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:00.090226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:00.948111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:02.257534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:54.296962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:55.141727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:56.390868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:57.361846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:58.386044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:59.296717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:00.191373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:01.053999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:02.344563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:54.398810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:55.234150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:56.500735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:57.483057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:58.508525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:59.403822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:00.296068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:01.179797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:02.444414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:54.483225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:55.316646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:56.597566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:57.567042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:58.614354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:59.509300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:00.392868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:01.316517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:02.516147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:54.567190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:55.406516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:56.702982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:57.654021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:58.705278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:59.611723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:00.473815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:01.416440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:02.615556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:54.670756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:55.531592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:56.813763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:57.771317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:58.812243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:27:59.713374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:00.571429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:01.514040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:28:08.478947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분계남여배우자(계)배우자(남)배우자(여)부모(계)부모(남)부모(여)자녀(계)자녀(남)자녀(여)손자녀(계)손자녀(남)손자녀(여)
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
계1.0001.0000.6600.6600.6600.6600.6601.0001.0001.0001.0001.0001.0000.0000.0000.000
남1.0000.6601.0000.6600.6600.6600.6601.0001.0001.0001.0001.0001.0000.0000.0000.000
여1.0000.6600.6601.0000.6600.6600.6601.0001.0001.0001.0001.0001.0000.0000.0000.000
배우자(계)1.0000.6600.6600.6601.0000.6600.6601.0001.0001.0001.0001.0001.0000.0000.0000.000
배우자(남)1.0000.6600.6600.6600.6601.0000.6601.0001.0001.0001.0001.0001.0000.0000.0000.000
배우자(여)1.0000.6600.6600.6600.6600.6601.0001.0001.0001.0001.0001.0001.0000.0000.0000.000
부모(계)1.0001.0001.0001.0001.0001.0001.0001.0000.8540.9190.6550.6390.6520.0000.0000.000
부모(남)1.0001.0001.0001.0001.0001.0001.0000.8541.0000.7150.6780.6750.6510.0000.0000.000
부모(여)1.0001.0001.0001.0001.0001.0001.0000.9190.7151.0000.8870.8800.8950.0000.0000.000
자녀(계)1.0001.0001.0001.0001.0001.0001.0000.6550.6780.8871.0000.9960.9840.0000.1450.000
자녀(남)1.0001.0001.0001.0001.0001.0001.0000.6390.6750.8800.9961.0000.9720.0000.3070.000
자녀(여)1.0001.0001.0001.0001.0001.0001.0000.6520.6510.8950.9840.9721.0000.0000.0000.000
손자녀(계)1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.000
손자녀(남)1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1450.3070.0001.0001.0000.425
손자녀(여)1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.4251.000
2023-12-12T23:28:08.608944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부모(계)부모(남)손자녀(계)손자녀(남)손자녀(여)부모(여)
부모(계)1.0000.8620.0000.0000.0000.913
부모(남)0.8621.0000.0000.0000.0000.742
손자녀(계)0.0000.0001.0000.9840.9840.000
손자녀(남)0.0000.0000.9841.0000.2780.000
손자녀(여)0.0000.0000.9840.2781.0000.000
부모(여)0.9130.7420.0000.0000.0001.000
2023-12-12T23:28:08.706490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계남여배우자(계)배우자(남)배우자(여)자녀(계)자녀(남)자녀(여)부모(계)부모(남)부모(여)손자녀(계)손자녀(남)손자녀(여)
계1.0000.9700.9990.9950.9790.9950.8710.8610.8290.9500.9840.9670.0000.0000.000
남0.9701.0000.9660.9540.9240.9540.8910.8910.8300.9500.9840.9670.0000.0000.000
여0.9990.9661.0000.9970.9810.9960.8610.8520.8210.9500.9840.9670.0000.0000.000
배우자(계)0.9950.9540.9971.0000.9871.0000.8360.8290.7960.9500.9840.9670.0000.0000.000
배우자(남)0.9790.9240.9810.9871.0000.9870.8220.8200.7770.9500.9840.9670.0000.0000.000
배우자(여)0.9950.9540.9961.0000.9871.0000.8360.8290.7950.9500.9840.9670.0000.0000.000
자녀(계)0.8710.8910.8610.8360.8220.8361.0000.9810.9340.5730.6970.5610.0000.0750.000
자녀(남)0.8610.8910.8520.8290.8200.8290.9811.0000.8610.5560.6920.5490.0000.1910.000
자녀(여)0.8290.8300.8210.7960.7770.7950.9340.8611.0000.5700.6640.5750.0000.0000.000
부모(계)0.9500.9500.9500.9500.9500.9500.5730.5560.5701.0000.8620.9130.0000.0000.000
부모(남)0.9840.9840.9840.9840.9840.9840.6970.6920.6640.8621.0000.7420.0000.0000.000
부모(여)0.9670.9670.9670.9670.9670.9670.5610.5490.5750.9130.7421.0000.0000.0000.000
손자녀(계)0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.9840.984
손자녀(남)0.0000.0000.0000.0000.0000.0000.0750.1910.0000.0000.0000.0000.9841.0000.278
손자녀(여)0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9840.2781.000

Missing values

2023-12-12T23:28:02.746549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:28:03.001702image/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

구분계남여배우자(계)배우자(남)배우자(여)부모(계)부모(남)부모(여)자녀(계)자녀(남)자녀(여)손자녀(계)손자녀(남)손자녀(여)조부모(계)조부모(남)조부모(여)
018세미만28199000000462917000000
119세미만642000000642211000
236세미만442123909000392316000000
336세이상1486817000321000000
437세716615000862110000
538세18810716000211000000
639세28101820416000981000000
740세321121205150001165000000
841세462026285230001183000000
942세5517383773000020173000000
구분계남여배우자(계)배우자(남)배우자(여)부모(계)부모(남)부모(여)자녀(계)자녀(남)자녀(여)손자녀(계)손자녀(남)손자녀(여)조부모(계)조부모(남)조부모(여)
2356세5306946143725412000493019000000
2457세5775652153245487000493118000000
2558세6516658561143568101452619000000
2659세9368285467448626000553718000000
2760세1143103104097973906000422418000000
2861세140011412861186721114101583622000000
2962세140310313001469851384505694425000000
3063세141810913091507821425000594118000000
3164세159312714661532921440615473017000000
3265세이상607161582591346499315866340775610165525416094000000