Overview

Dataset statistics

Number of variables20
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 KiB
Average record size in memory183.1 B

Variable types

Text1
Numeric11
Categorical8

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 강도범죄자의 생활환경에 따른 직업별 범죄 통계임. (단위: 명)
Author대검찰청
URLhttps://www.data.go.kr/data/15085997/fileData.do

Alerts

계모무부 has constant value ""Constant
계부무모 is highly overall correlated with 생활정도_하류 and 15 other fieldsHigh correlation
사별 is highly overall correlated with 생활정도_하류 and 14 other fieldsHigh correlation
실모계부 is highly overall correlated with 생활정도_하류 and 16 other fieldsHigh correlation
계부모 is highly overall correlated with 생활정도_하류 and 16 other fieldsHigh correlation
미혼자부모관계_미상 is highly overall correlated with 생활정도_하류 and 15 other fieldsHigh correlation
실부계모 is highly overall correlated with 생활정도_하류 and 16 other fieldsHigh correlation
생활정도_상류 is highly overall correlated with 생활정도_하류 and 16 other fieldsHigh correlation
생활정도_하류 is highly overall correlated with 생활정도_중류 and 16 other fieldsHigh correlation
생활정도_중류 is highly overall correlated with 생활정도_하류 and 15 other fieldsHigh correlation
생활정도_미상 is highly overall correlated with 생활정도_하류 and 14 other fieldsHigh correlation
유배우자 is highly overall correlated with 생활정도_하류 and 14 other fieldsHigh correlation
동거 is highly overall correlated with 생활정도_하류 and 15 other fieldsHigh correlation
이혼 is highly overall correlated with 생활정도_하류 and 16 other fieldsHigh correlation
혼인관계_미상 is highly overall correlated with 생활정도_하류 and 14 other fieldsHigh correlation
실(양)부모 is highly overall correlated with 생활정도_하류 and 14 other fieldsHigh correlation
실부무모 is highly overall correlated with 생활정도_하류 and 13 other fieldsHigh correlation
실모무부 is highly overall correlated with 생활정도_하류 and 15 other fieldsHigh correlation
무부모 is highly overall correlated with 생활정도_하류 and 16 other fieldsHigh correlation
생활정도_상류 is highly imbalanced (63.4%)Imbalance
사별 is highly imbalanced (53.5%)Imbalance
계부모 is highly imbalanced (74.8%)Imbalance
실부계모 is highly imbalanced (53.4%)Imbalance
실모계부 is highly imbalanced (56.8%)Imbalance
계부무모 is highly imbalanced (79.9%)Imbalance
미혼자부모관계_미상 is highly imbalanced (79.9%)Imbalance
직업별 has unique valuesUnique
생활정도_하류 has 6 (18.8%) zerosZeros
생활정도_중류 has 8 (25.0%) zerosZeros
생활정도_미상 has 14 (43.8%) zerosZeros
유배우자 has 8 (25.0%) zerosZeros
동거 has 24 (75.0%) zerosZeros
이혼 has 17 (53.1%) zerosZeros
혼인관계_미상 has 14 (43.8%) zerosZeros
실(양)부모 has 9 (28.1%) zerosZeros
실부무모 has 24 (75.0%) zerosZeros
실모무부 has 18 (56.2%) zerosZeros
무부모 has 23 (71.9%) zerosZeros

Reproduction

Analysis started2023-12-12 06:29:44.907938
Analysis finished2023-12-12 06:29:59.360943
Duration14.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

직업별
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T15:29:59.533806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.625
Min length2

Characters and Unicode

Total characters116
Distinct characters58
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

Unique32 ?
Unique (%)100.0%

Sample

1st row농·임·수산업
2nd row제조업
3rd row건설업
4th row도·소매업
5th row무역업
ValueCountFrequency (%)
농·임·수산업 1
 
3.1%
제조업 1
 
3.1%
무직자 1
 
3.1%
공익요원 1
 
3.1%
주부 1
 
3.1%
학생 1
 
3.1%
공무원 1
 
3.1%
기타전문직 1
 
3.1%
예술인 1
 
3.1%
종교가 1
 
3.1%
Other values (22) 22
68.8%
2023-12-12T15:30:00.025089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12
 
10.3%
7
 
6.0%
7
 
6.0%
6
 
5.2%
4
 
3.4%
4
 
3.4%
3
 
2.6%
3
 
2.6%
3
 
2.6%
· 3
 
2.6%
Other values (48) 64
55.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 113
97.4%
Other Punctuation 3
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12
 
10.6%
7
 
6.2%
7
 
6.2%
6
 
5.3%
4
 
3.5%
4
 
3.5%
3
 
2.7%
3
 
2.7%
3
 
2.7%
3
 
2.7%
Other values (47) 61
54.0%
Other Punctuation
ValueCountFrequency (%)
· 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 113
97.4%
Common 3
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12
 
10.6%
7
 
6.2%
7
 
6.2%
6
 
5.3%
4
 
3.5%
4
 
3.5%
3
 
2.7%
3
 
2.7%
3
 
2.7%
3
 
2.7%
Other values (47) 61
54.0%
Common
ValueCountFrequency (%)
· 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 113
97.4%
None 3
 
2.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
12
 
10.6%
7
 
6.2%
7
 
6.2%
6
 
5.3%
4
 
3.5%
4
 
3.5%
3
 
2.7%
3
 
2.7%
3
 
2.7%
3
 
2.7%
Other values (47) 61
54.0%
None
ValueCountFrequency (%)
· 3
100.0%

생활정도_하류
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.5
Minimum0
Maximum516
Zeros6
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T15:30:00.174133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q311
95-th percentile77.95
Maximum516
Range516
Interquartile range (IQR)10

Descriptive statistics

Standard deviation91.960721
Coefficient of variation (CV)3.0151056
Kurtosis27.120779
Mean30.5
Median Absolute Deviation (MAD)3
Skewness5.0550263
Sum976
Variance8456.7742
MonotonicityNot monotonic
2023-12-12T15:30:00.292335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 6
18.8%
1 4
12.5%
5 3
9.4%
2 3
9.4%
7 2
 
6.2%
4 2
 
6.2%
3 2
 
6.2%
73 1
 
3.1%
47 1
 
3.1%
516 1
 
3.1%
Other values (7) 7
21.9%
ValueCountFrequency (%)
0 6
18.8%
1 4
12.5%
2 3
9.4%
3 2
 
6.2%
4 2
 
6.2%
5 3
9.4%
6 1
 
3.1%
7 2
 
6.2%
9 1
 
3.1%
17 1
 
3.1%
ValueCountFrequency (%)
516 1
3.1%
84 1
3.1%
73 1
3.1%
71 1
3.1%
64 1
3.1%
47 1
3.1%
36 1
3.1%
17 1
3.1%
9 1
3.1%
7 2
6.2%

생활정도_중류
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.53125
Minimum0
Maximum109
Zeros8
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T15:30:00.429722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median2
Q35.5
95-th percentile47.3
Maximum109
Range109
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation22.146119
Coefficient of variation (CV)2.1028956
Kurtosis12.824598
Mean10.53125
Median Absolute Deviation (MAD)2
Skewness3.3660502
Sum337
Variance490.4506
MonotonicityNot monotonic
2023-12-12T15:30:00.896865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 8
25.0%
1 6
18.8%
3 3
 
9.4%
2 3
 
9.4%
5 2
 
6.2%
4 2
 
6.2%
17 1
 
3.1%
41 1
 
3.1%
30 1
 
3.1%
15 1
 
3.1%
Other values (4) 4
12.5%
ValueCountFrequency (%)
0 8
25.0%
1 6
18.8%
2 3
 
9.4%
3 3
 
9.4%
4 2
 
6.2%
5 2
 
6.2%
7 1
 
3.1%
15 1
 
3.1%
17 1
 
3.1%
24 1
 
3.1%
ValueCountFrequency (%)
109 1
3.1%
55 1
3.1%
41 1
3.1%
30 1
3.1%
24 1
3.1%
17 1
3.1%
15 1
3.1%
7 1
3.1%
5 2
6.2%
4 2
6.2%

생활정도_상류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
0
28 
1
 
2
2
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)6.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 28
87.5%
1 2
 
6.2%
2 1
 
3.1%
4 1
 
3.1%

Length

2023-12-12T15:30:01.013830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:30:01.113881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
87.5%
1 2
 
6.2%
2 1
 
3.1%
4 1
 
3.1%

생활정도_미상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5
Minimum0
Maximum49
Zeros14
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T15:30:01.214822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile33.8
Maximum49
Range49
Interquartile range (IQR)4

Descriptive statistics

Standard deviation12.239545
Coefficient of variation (CV)2.2253717
Kurtosis8.3145243
Mean5.5
Median Absolute Deviation (MAD)1
Skewness2.9485483
Sum176
Variance149.80645
MonotonicityNot monotonic
2023-12-12T15:30:01.362273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 14
43.8%
1 7
21.9%
2 2
 
6.2%
4 2
 
6.2%
14 1
 
3.1%
23 1
 
3.1%
13 1
 
3.1%
6 1
 
3.1%
5 1
 
3.1%
47 1
 
3.1%
ValueCountFrequency (%)
0 14
43.8%
1 7
21.9%
2 2
 
6.2%
4 2
 
6.2%
5 1
 
3.1%
6 1
 
3.1%
13 1
 
3.1%
14 1
 
3.1%
23 1
 
3.1%
47 1
 
3.1%
ValueCountFrequency (%)
49 1
 
3.1%
47 1
 
3.1%
23 1
 
3.1%
14 1
 
3.1%
13 1
 
3.1%
6 1
 
3.1%
5 1
 
3.1%
4 2
 
6.2%
2 2
 
6.2%
1 7
21.9%

유배우자
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8125
Minimum0
Maximum69
Zeros8
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T15:30:01.482430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median2
Q36
95-th percentile47.15
Maximum69
Range69
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation17.134078
Coefficient of variation (CV)1.9442925
Kurtosis6.6723262
Mean8.8125
Median Absolute Deviation (MAD)2
Skewness2.6691409
Sum282
Variance293.57661
MonotonicityNot monotonic
2023-12-12T15:30:01.632099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 8
25.0%
1 5
15.6%
3 4
12.5%
2 4
12.5%
6 2
 
6.2%
9 2
 
6.2%
5 1
 
3.1%
35 1
 
3.1%
62 1
 
3.1%
32 1
 
3.1%
Other values (3) 3
 
9.4%
ValueCountFrequency (%)
0 8
25.0%
1 5
15.6%
2 4
12.5%
3 4
12.5%
4 1
 
3.1%
5 1
 
3.1%
6 2
 
6.2%
9 2
 
6.2%
20 1
 
3.1%
32 1
 
3.1%
ValueCountFrequency (%)
69 1
 
3.1%
62 1
 
3.1%
35 1
 
3.1%
32 1
 
3.1%
20 1
 
3.1%
9 2
6.2%
6 2
6.2%
5 1
 
3.1%
4 1
 
3.1%
3 4
12.5%

동거
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1875
Minimum0
Maximum18
Zeros24
Zeros (%)75.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T15:30:01.794793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.25
95-th percentile4.9
Maximum18
Range18
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation3.3738797
Coefficient of variation (CV)2.8411619
Kurtosis21.02842
Mean1.1875
Median Absolute Deviation (MAD)0
Skewness4.3482559
Sum38
Variance11.383065
MonotonicityNot monotonic
2023-12-12T15:30:01.918490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 24
75.0%
2 3
 
9.4%
1 1
 
3.1%
3 1
 
3.1%
6 1
 
3.1%
4 1
 
3.1%
18 1
 
3.1%
ValueCountFrequency (%)
0 24
75.0%
1 1
 
3.1%
2 3
 
9.4%
3 1
 
3.1%
4 1
 
3.1%
6 1
 
3.1%
18 1
 
3.1%
ValueCountFrequency (%)
18 1
 
3.1%
6 1
 
3.1%
4 1
 
3.1%
3 1
 
3.1%
2 3
 
9.4%
1 1
 
3.1%
0 24
75.0%

이혼
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.90625
Minimum0
Maximum72
Zeros17
Zeros (%)53.1%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T15:30:02.026154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile12.6
Maximum72
Range72
Interquartile range (IQR)2

Descriptive statistics

Standard deviation12.895009
Coefficient of variation (CV)3.3011222
Kurtosis27.180659
Mean3.90625
Median Absolute Deviation (MAD)0
Skewness5.0863023
Sum125
Variance166.28125
MonotonicityNot monotonic
2023-12-12T15:30:02.144881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 17
53.1%
1 5
 
15.6%
2 4
 
12.5%
4 2
 
6.2%
6 1
 
3.1%
17 1
 
3.1%
9 1
 
3.1%
72 1
 
3.1%
ValueCountFrequency (%)
0 17
53.1%
1 5
 
15.6%
2 4
 
12.5%
4 2
 
6.2%
6 1
 
3.1%
9 1
 
3.1%
17 1
 
3.1%
72 1
 
3.1%
ValueCountFrequency (%)
72 1
 
3.1%
17 1
 
3.1%
9 1
 
3.1%
6 1
 
3.1%
4 2
 
6.2%
2 4
 
12.5%
1 5
 
15.6%
0 17
53.1%

사별
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
0
26 
1
3
 
1
10
 
1

Length

Max length2
Median length1
Mean length1.03125
Min length1

Unique

Unique2 ?
Unique (%)6.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 26
81.2%
1 4
 
12.5%
3 1
 
3.1%
10 1
 
3.1%

Length

2023-12-12T15:30:02.292948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:30:02.407803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 26
81.2%
1 4
 
12.5%
3 1
 
3.1%
10 1
 
3.1%

혼인관계_미상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4375
Minimum0
Maximum47
Zeros14
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T15:30:02.528431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile33.8
Maximum47
Range47
Interquartile range (IQR)4

Descriptive statistics

Standard deviation12.013266
Coefficient of variation (CV)2.2093362
Kurtosis8.1295068
Mean5.4375
Median Absolute Deviation (MAD)1
Skewness2.9198432
Sum174
Variance144.31855
MonotonicityNot monotonic
2023-12-12T15:30:02.644896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 14
43.8%
1 7
21.9%
2 2
 
6.2%
4 2
 
6.2%
47 2
 
6.2%
14 1
 
3.1%
23 1
 
3.1%
13 1
 
3.1%
6 1
 
3.1%
5 1
 
3.1%
ValueCountFrequency (%)
0 14
43.8%
1 7
21.9%
2 2
 
6.2%
4 2
 
6.2%
5 1
 
3.1%
6 1
 
3.1%
13 1
 
3.1%
14 1
 
3.1%
23 1
 
3.1%
47 2
 
6.2%
ValueCountFrequency (%)
47 2
 
6.2%
23 1
 
3.1%
14 1
 
3.1%
13 1
 
3.1%
6 1
 
3.1%
5 1
 
3.1%
4 2
 
6.2%
2 2
 
6.2%
1 7
21.9%
0 14
43.8%

실(양)부모
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.03125
Minimum0
Maximum286
Zeros9
Zeros (%)28.1%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T15:30:02.770915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q35.25
95-th percentile76.6
Maximum286
Range286
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation53.621423
Coefficient of variation (CV)2.9738051
Kurtosis21.521723
Mean18.03125
Median Absolute Deviation (MAD)1.5
Skewness4.4715127
Sum577
Variance2875.2571
MonotonicityNot monotonic
2023-12-12T15:30:02.971579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 9
28.1%
1 7
21.9%
3 5
15.6%
2 2
 
6.2%
10 1
 
3.1%
26 1
 
3.1%
6 1
 
3.1%
24 1
 
3.1%
46 1
 
3.1%
5 1
 
3.1%
Other values (3) 3
 
9.4%
ValueCountFrequency (%)
0 9
28.1%
1 7
21.9%
2 2
 
6.2%
3 5
15.6%
5 1
 
3.1%
6 1
 
3.1%
10 1
 
3.1%
24 1
 
3.1%
26 1
 
3.1%
34 1
 
3.1%
ValueCountFrequency (%)
286 1
 
3.1%
114 1
 
3.1%
46 1
 
3.1%
34 1
 
3.1%
26 1
 
3.1%
24 1
 
3.1%
10 1
 
3.1%
6 1
 
3.1%
5 1
 
3.1%
3 5
15.6%

계부모
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
0
30 
1
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)6.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 30
93.8%
1 1
 
3.1%
2 1
 
3.1%

Length

2023-12-12T15:30:03.110070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:30:03.229915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
93.8%
1 1
 
3.1%
2 1
 
3.1%

실부계모
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
0
27 
1
14
 
1

Length

Max length2
Median length1
Mean length1.03125
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 27
84.4%
1 4
 
12.5%
14 1
 
3.1%

Length

2023-12-12T15:30:03.340267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:30:03.458118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
84.4%
1 4
 
12.5%
14 1
 
3.1%

실부무모
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.96875
Minimum0
Maximum38
Zeros24
Zeros (%)75.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T15:30:03.553468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.25
95-th percentile7.45
Maximum38
Range38
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation6.8602848
Coefficient of variation (CV)3.4845891
Kurtosis26.521828
Mean1.96875
Median Absolute Deviation (MAD)0
Skewness5.0032896
Sum63
Variance47.063508
MonotonicityNot monotonic
2023-12-12T15:30:03.662219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 24
75.0%
1 2
 
6.2%
2 2
 
6.2%
8 1
 
3.1%
7 1
 
3.1%
38 1
 
3.1%
4 1
 
3.1%
ValueCountFrequency (%)
0 24
75.0%
1 2
 
6.2%
2 2
 
6.2%
4 1
 
3.1%
7 1
 
3.1%
8 1
 
3.1%
38 1
 
3.1%
ValueCountFrequency (%)
38 1
 
3.1%
8 1
 
3.1%
7 1
 
3.1%
4 1
 
3.1%
2 2
 
6.2%
1 2
 
6.2%
0 24
75.0%

실모계부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
0
27 
2
 
2
1
 
2
7
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 27
84.4%
2 2
 
6.2%
1 2
 
6.2%
7 1
 
3.1%

Length

2023-12-12T15:30:03.802430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:30:03.920930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
84.4%
2 2
 
6.2%
1 2
 
6.2%
7 1
 
3.1%

실모무부
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3125
Minimum0
Maximum64
Zeros18
Zeros (%)56.2%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T15:30:04.018141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.5
95-th percentile7.8
Maximum64
Range64
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation11.317806
Coefficient of variation (CV)3.4166963
Kurtosis29.065847
Mean3.3125
Median Absolute Deviation (MAD)0
Skewness5.295414
Sum106
Variance128.09274
MonotonicityNot monotonic
2023-12-12T15:30:04.130202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 18
56.2%
1 6
 
18.8%
3 2
 
6.2%
5 2
 
6.2%
4 1
 
3.1%
6 1
 
3.1%
10 1
 
3.1%
64 1
 
3.1%
ValueCountFrequency (%)
0 18
56.2%
1 6
 
18.8%
3 2
 
6.2%
4 1
 
3.1%
5 2
 
6.2%
6 1
 
3.1%
10 1
 
3.1%
64 1
 
3.1%
ValueCountFrequency (%)
64 1
 
3.1%
10 1
 
3.1%
6 1
 
3.1%
5 2
 
6.2%
4 1
 
3.1%
3 2
 
6.2%
1 6
 
18.8%
0 18
56.2%

계부무모
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
0
31 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 31
96.9%
1 1
 
3.1%

Length

2023-12-12T15:30:04.255857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:30:04.385762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31
96.9%
1 1
 
3.1%

계모무부
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
0
32 

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 32
100.0%

Length

2023-12-12T15:30:04.508288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:30:04.602146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 32
100.0%

무부모
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.40625
Minimum0
Maximum45
Zeros23
Zeros (%)71.9%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T15:30:04.734437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile8.25
Maximum45
Range45
Interquartile range (IQR)1

Descriptive statistics

Standard deviation8.1314181
Coefficient of variation (CV)3.3792906
Kurtosis26.222248
Mean2.40625
Median Absolute Deviation (MAD)0
Skewness4.9693854
Sum77
Variance66.11996
MonotonicityNot monotonic
2023-12-12T15:30:04.871593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 23
71.9%
1 3
 
9.4%
5 1
 
3.1%
11 1
 
3.1%
6 1
 
3.1%
3 1
 
3.1%
45 1
 
3.1%
4 1
 
3.1%
ValueCountFrequency (%)
0 23
71.9%
1 3
 
9.4%
3 1
 
3.1%
4 1
 
3.1%
5 1
 
3.1%
6 1
 
3.1%
11 1
 
3.1%
45 1
 
3.1%
ValueCountFrequency (%)
45 1
 
3.1%
11 1
 
3.1%
6 1
 
3.1%
5 1
 
3.1%
4 1
 
3.1%
3 1
 
3.1%
1 3
 
9.4%
0 23
71.9%

미혼자부모관계_미상
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
0
31 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 31
96.9%
3 1
 
3.1%

Length

2023-12-12T15:30:04.986923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:30:05.082234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31
96.9%
3 1
 
3.1%

Interactions

2023-12-12T15:29:57.648609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:46.103441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:47.453590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:48.692459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:49.886027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:51.010138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:52.251400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:53.457433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:54.634213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:55.447843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:56.482466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:57.773658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:46.186377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:47.563643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:48.791592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:49.987934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:51.120754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:52.353299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:53.551260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:54.702224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:55.523085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:56.569040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:57.867022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:46.273376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:47.681980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:48.895897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:50.089883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:51.242255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:52.470034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:53.664274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:54.771187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:55.596401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:56.648885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:57.962937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:46.372578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:47.791867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:49.020600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:50.192553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:51.348868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:52.573018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:53.742768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:54.839980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:55.673649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:56.776070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:58.048531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:46.487477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:47.935423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:49.135020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:50.314900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:51.485251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:52.686982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:53.851348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:54.912922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:55.768838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:56.891114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:58.146716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:46.582765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:48.046052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:49.238848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:50.428861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:51.584192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:52.796872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:53.943024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:54.988296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:55.863370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:57.036665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:58.270372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:46.681993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:48.162513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:49.339171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:50.527690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:51.705523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:52.907415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:54.285219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:55.068575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:55.950252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:57.157143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:58.362463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:46.770274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:48.245668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:49.444970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:50.638216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:51.794410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:53.011857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:54.346499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:55.139021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:56.044466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:57.255945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:58.461389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:46.850730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:48.358507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:49.551771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:50.733086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:51.888175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:53.129049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:54.429374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:55.226950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:56.146745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:57.348530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:58.553144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:46.955823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:48.482944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:49.671822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:50.829192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:51.996951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:53.230726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:54.505934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:55.308009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:56.285882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:57.462884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:58.664222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:47.061834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:48.588912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:49.776466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:50.926557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:52.133933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:53.355724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:54.573956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:55.380387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:56.398266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:57.564636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:30:05.163438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
직업별생활정도_하류생활정도_중류생활정도_상류생활정도_미상유배우자동거이혼사별혼인관계_미상실(양)부모계부모실부계모실부무모실모계부실모무부계부무모무부모미혼자부모관계_미상
직업별1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
생활정도_하류1.0001.0000.9950.6740.7770.8730.8110.7820.7250.7770.7440.9640.9590.7440.7030.9641.0000.8491.000
생활정도_중류1.0000.9951.0000.9350.8980.9170.8650.8900.8100.8980.9711.0000.9800.9350.9371.0001.0000.9351.000
생활정도_상류1.0000.6740.9351.0000.7691.0000.7900.8580.8610.7690.9010.6470.6740.9010.9570.6471.0000.9011.000
생활정도_미상1.0000.7770.8980.7691.0000.9130.9520.7030.5821.0000.7160.6680.6100.7160.8170.6680.5280.7610.528
유배우자1.0000.8730.9171.0000.9131.0000.9020.9170.8610.9130.7970.7070.7850.8410.9170.7071.0000.9481.000
동거1.0000.8110.8650.7900.9520.9021.0000.8210.6470.9520.7410.6820.7550.8350.6270.6821.0000.8211.000
이혼1.0000.7820.8900.8580.7030.9170.8211.0000.9870.7030.9370.6470.6490.9770.9370.6471.0000.9951.000
사별1.0000.7250.8100.8610.5820.8610.6470.9871.0000.5820.8770.6480.6550.9030.9380.6481.0000.9811.000
혼인관계_미상1.0000.7770.8980.7691.0000.9130.9520.7030.5821.0000.7160.6680.6100.7160.8170.6680.5280.7610.528
실(양)부모1.0000.7440.9710.9010.7160.7970.7410.9370.8770.7161.0001.0000.7180.9880.9601.0001.0000.9011.000
계부모1.0000.9641.0000.6470.6680.7070.6820.6470.6480.6681.0001.0000.9640.7870.7871.0001.0000.6471.000
실부계모1.0000.9590.9800.6740.6100.7850.7550.6490.6550.6100.7180.9641.0000.6740.6750.9641.0000.6741.000
실부무모1.0000.7440.9350.9010.7160.8410.8350.9770.9030.7160.9880.7870.6741.0000.9770.7871.0000.9371.000
실모계부1.0000.7030.9370.9570.8170.9170.6270.9370.9380.8170.9600.7870.6750.9771.0000.7871.0000.9371.000
실모무부1.0000.9641.0000.6470.6680.7070.6820.6470.6480.6681.0001.0000.9640.7870.7871.0001.0000.6471.000
계부무모1.0001.0001.0001.0000.5281.0001.0001.0001.0000.5281.0001.0001.0001.0001.0001.0001.0001.0000.659
무부모1.0000.8490.9350.9010.7610.9480.8210.9950.9810.7610.9010.6470.6740.9370.9370.6471.0001.0001.000
미혼자부모관계_미상1.0001.0001.0001.0000.5281.0001.0001.0001.0000.5281.0001.0001.0001.0001.0001.0000.6591.0001.000
2023-12-12T15:30:05.350502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계부무모사별실모계부계부모미혼자부모관계_미상실부계모생활정도_상류
계부무모1.0000.9660.9660.9830.4570.9830.966
사별0.9661.0000.6680.6590.9660.6680.519
실모계부0.9660.6681.0000.8300.9660.6930.721
계부모0.9830.6590.8301.0000.9830.7650.658
미혼자부모관계_미상0.4570.9660.9660.9831.0000.9830.966
실부계모0.9830.6680.6930.7650.9831.0000.691
생활정도_상류0.9660.5190.7210.6580.9660.6911.000
2023-12-12T15:30:05.501545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생활정도_하류생활정도_중류생활정도_미상유배우자동거이혼혼인관계_미상실(양)부모실부무모실모무부무부모생활정도_상류사별계부모실부계모실모계부계부무모미혼자부모관계_미상
생활정도_하류1.0000.5880.6560.6880.6070.7870.6570.7990.7090.7460.6550.6910.7530.7650.7500.7270.9830.983
생활정도_중류0.5881.0000.5920.6680.6490.6440.5930.6630.4630.7160.7200.7990.6350.9470.7840.8030.9310.931
생활정도_미상0.6560.5921.0000.6490.5900.8381.0000.4930.5040.6550.5770.7050.4970.6150.5430.7670.6060.606
유배우자0.6880.6680.6491.0000.6960.7150.6500.5330.4530.4650.5310.9450.7470.5760.6760.8380.9130.913
동거0.6070.6490.5900.6961.0000.6210.5910.6430.4260.6170.5420.7320.5650.6330.7310.5430.9490.949
이혼0.7870.6440.8380.7150.6211.0000.8390.5120.5060.7030.6130.5140.8450.6570.6610.6640.9660.966
혼인관계_미상0.6570.5931.0000.6500.5910.8391.0000.4940.5050.6560.5780.7050.4970.6150.5430.7670.6060.606
실(양)부모0.7990.6630.4930.5330.6430.5120.4941.0000.6510.6470.6520.5860.5430.9830.7450.7300.9660.966
실부무모0.7090.4630.5040.4530.4260.5060.5050.6511.0000.6780.7170.5860.5900.8300.6910.7950.9660.966
실모무부0.7460.7160.6550.4650.6170.7030.6560.6470.6781.0000.7040.6580.6591.0000.7650.8300.9830.983
무부모0.6550.7200.5770.5310.5420.6130.5780.6520.7170.7041.0000.5860.8130.6580.6910.6650.9660.966
생활정도_상류0.6910.7990.7050.9450.7320.5140.7050.5860.5860.6580.5861.0000.5190.6580.6910.7210.9660.966
사별0.7530.6350.4970.7470.5650.8450.4970.5430.5900.6590.8130.5191.0000.6590.6680.6680.9660.966
계부모0.7650.9470.6150.5760.6330.6570.6150.9830.8301.0000.6580.6580.6591.0000.7650.8300.9830.983
실부계모0.7500.7840.5430.6760.7310.6610.5430.7450.6910.7650.6910.6910.6680.7651.0000.6930.9830.983
실모계부0.7270.8030.7670.8380.5430.6640.7670.7300.7950.8300.6650.7210.6680.8300.6931.0000.9660.966
계부무모0.9830.9310.6060.9130.9490.9660.6060.9660.9660.9830.9660.9660.9660.9830.9830.9661.0000.457
미혼자부모관계_미상0.9830.9310.6060.9130.9490.9660.6060.9660.9660.9830.9660.9660.9660.9830.9830.9660.4571.000

Missing values

2023-12-12T15:29:58.887866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:29:59.224131image/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

직업별생활정도_하류생활정도_중류생활정도_상류생활정도_미상유배우자동거이혼사별혼인관계_미상실(양)부모계부모실부계모실부무모실모계부실모무부계부무모계모무부무부모미혼자부모관계_미상
0농·임·수산업7102302122000000000
1제조업1000000100000000000
2건설업5300601001000000000
3도·소매업1500500000000000010
4무역업4001200012000000000
5요식업2500310003000000000
6유흥업3301002010010030000
7부동산업0102100020000000000
8차량정비업2000100001000000000
9노점0200200000000000000
직업별생활정도_하류생활정도_중류생활정도_상류생활정도_미상유배우자동거이혼사별혼인관계_미상실(양)부모계부모실부계모실부무모실모계부실모무부계부무모계모무부무부모미혼자부모관계_미상
22변호사0001000010000000000
23종교가0100000000000010000
24예술인1000000001000000000
25기타전문직9701621015010010000
26공무원0401300011000000000
27학생845505101051141171100030
28주부5104402040000000000
29공익요원1200000003000000000
30무직자51610944769187210472862143876410453
31미상4724149202404734004150040