Overview

Dataset statistics

Number of variables20
Number of observations45
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.0 KiB
Average record size in memory181.9 B

Variable types

Text1
Numeric17
Categorical2

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며,이 중 본 데이터는 성폭력범죄자의 생활환경에 따른 직업별 범죄 통계임. (단위: 명)
Author대검찰청
URLhttps://www.data.go.kr/data/15086000/fileData.do

Alerts

생활정도_하류 is highly overall correlated with 생활정도_중류 and 17 other fieldsHigh correlation
생활정도_중류 is highly overall correlated with 생활정도_하류 and 17 other fieldsHigh correlation
생활정도_상류 is highly overall correlated with 생활정도_하류 and 10 other fieldsHigh correlation
생활정도_미상 is highly overall correlated with 생활정도_하류 and 17 other fieldsHigh correlation
유배우자 is highly overall correlated with 생활정도_하류 and 15 other fieldsHigh correlation
동거 is highly overall correlated with 생활정도_하류 and 16 other fieldsHigh correlation
이혼 is highly overall correlated with 생활정도_하류 and 14 other fieldsHigh correlation
사별 is highly overall correlated with 생활정도_하류 and 13 other fieldsHigh correlation
혼인관계_미상 is highly overall correlated with 생활정도_하류 and 17 other fieldsHigh correlation
실(양)부모 is highly overall correlated with 생활정도_하류 and 17 other fieldsHigh correlation
계부모 is highly overall correlated with 생활정도_하류 and 16 other fieldsHigh correlation
실부계모 is highly overall correlated with 생활정도_하류 and 15 other fieldsHigh correlation
실부무모 is highly overall correlated with 생활정도_하류 and 15 other fieldsHigh correlation
실모계부 is highly overall correlated with 생활정도_하류 and 14 other fieldsHigh correlation
실모무부 is highly overall correlated with 생활정도_하류 and 17 other fieldsHigh correlation
무부모 is highly overall correlated with 생활정도_하류 and 16 other fieldsHigh correlation
미혼자부모관계_미상 is highly overall correlated with 생활정도_하류 and 11 other fieldsHigh correlation
계부무모 is highly overall correlated with 생활정도_하류 and 17 other fieldsHigh correlation
계모무부 is highly overall correlated with 생활정도_하류 and 17 other fieldsHigh correlation
계부무모 is highly imbalanced (73.8%)Imbalance
계모무부 is highly imbalanced (58.1%)Imbalance
직업별 has unique valuesUnique
생활정도_하류 has 2 (4.4%) zerosZeros
생활정도_중류 has 1 (2.2%) zerosZeros
생활정도_상류 has 13 (28.9%) zerosZeros
유배우자 has 3 (6.7%) zerosZeros
동거 has 14 (31.1%) zerosZeros
이혼 has 6 (13.3%) zerosZeros
사별 has 17 (37.8%) zerosZeros
실(양)부모 has 2 (4.4%) zerosZeros
계부모 has 36 (80.0%) zerosZeros
실부계모 has 33 (73.3%) zerosZeros
실부무모 has 19 (42.2%) zerosZeros
실모계부 has 29 (64.4%) zerosZeros
실모무부 has 8 (17.8%) zerosZeros
무부모 has 13 (28.9%) zerosZeros
미혼자부모관계_미상 has 35 (77.8%) zerosZeros

Reproduction

Analysis started2023-12-12 21:53:08.387528
Analysis finished2023-12-12 21:53:38.379337
Duration29.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

직업별
Text

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size492.0 B
2023-12-13T06:53:38.551361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.6888889
Min length2

Characters and Unicode

Total characters166
Distinct characters78
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)100.0%

Sample

1st row농·임·수산업
2nd row제조업
3rd row건설업
4th row도·소매업
5th row무역업
ValueCountFrequency (%)
농·임·수산업 1
 
2.2%
국공영기업체직원 1
 
2.2%
금융기관직원 1
 
2.2%
유흥업종사자 1
 
2.2%
요식업종사자 1
 
2.2%
일용노동자 1
 
2.2%
기타피고용자 1
 
2.2%
의사 1
 
2.2%
변호사 1
 
2.2%
교수 1
 
2.2%
Other values (35) 35
77.8%
2023-12-13T06:53:38.933448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16
 
9.6%
11
 
6.6%
9
 
5.4%
6
 
3.6%
6
 
3.6%
4
 
2.4%
· 4
 
2.4%
4
 
2.4%
4
 
2.4%
3
 
1.8%
Other values (68) 99
59.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 160
96.4%
Other Punctuation 4
 
2.4%
Open Punctuation 1
 
0.6%
Close Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
16
 
10.0%
11
 
6.9%
9
 
5.6%
6
 
3.8%
6
 
3.8%
4
 
2.5%
4
 
2.5%
4
 
2.5%
3
 
1.9%
3
 
1.9%
Other values (65) 94
58.8%
Other Punctuation
ValueCountFrequency (%)
· 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 160
96.4%
Common 6
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
16
 
10.0%
11
 
6.9%
9
 
5.6%
6
 
3.8%
6
 
3.8%
4
 
2.5%
4
 
2.5%
4
 
2.5%
3
 
1.9%
3
 
1.9%
Other values (65) 94
58.8%
Common
ValueCountFrequency (%)
· 4
66.7%
( 1
 
16.7%
) 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 160
96.4%
None 4
 
2.4%
ASCII 2
 
1.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
16
 
10.0%
11
 
6.9%
9
 
5.6%
6
 
3.8%
6
 
3.8%
4
 
2.5%
4
 
2.5%
4
 
2.5%
3
 
1.9%
3
 
1.9%
Other values (65) 94
58.8%
None
ValueCountFrequency (%)
· 4
100.0%
ASCII
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

생활정도_하류
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.4
Minimum0
Maximum3636
Zeros2
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T06:53:39.074150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.2
Q110
median38
Q3140
95-th percentile1442
Maximum3636
Range3636
Interquartile range (IQR)130

Descriptive statistics

Standard deviation675.02244
Coefficient of variation (CV)2.3164806
Kurtosis14.107018
Mean291.4
Median Absolute Deviation (MAD)31
Skewness3.5114414
Sum13113
Variance455655.29
MonotonicityNot monotonic
2023-12-13T06:53:39.212885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
4 4
 
8.9%
38 3
 
6.7%
7 2
 
4.4%
11 2
 
4.4%
0 2
 
4.4%
10 1
 
2.2%
37 1
 
2.2%
169 1
 
2.2%
839 1
 
2.2%
1190 1
 
2.2%
Other values (27) 27
60.0%
ValueCountFrequency (%)
0 2
4.4%
1 1
 
2.2%
2 1
 
2.2%
4 4
8.9%
7 2
4.4%
8 1
 
2.2%
10 1
 
2.2%
11 2
4.4%
13 1
 
2.2%
14 1
 
2.2%
ValueCountFrequency (%)
3636 1
2.2%
2046 1
2.2%
1505 1
2.2%
1190 1
2.2%
1028 1
2.2%
839 1
2.2%
823 1
2.2%
330 1
2.2%
324 1
2.2%
177 1
2.2%

생활정도_중류
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean287.46667
Minimum0
Maximum3003
Zeros1
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T06:53:39.349750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.8
Q117
median41
Q3134
95-th percentile1860.4
Maximum3003
Range3003
Interquartile range (IQR)117

Descriptive statistics

Standard deviation651.93476
Coefficient of variation (CV)2.2678621
Kurtosis9.1059893
Mean287.46667
Median Absolute Deviation (MAD)30
Skewness3.0634596
Sum12936
Variance425018.94
MonotonicityNot monotonic
2023-12-13T06:53:39.494449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
9 3
 
6.7%
17 2
 
4.4%
28 2
 
4.4%
16 2
 
4.4%
454 1
 
2.2%
112 1
 
2.2%
209 1
 
2.2%
696 1
 
2.2%
49 1
 
2.2%
2 1
 
2.2%
Other values (30) 30
66.7%
ValueCountFrequency (%)
0 1
 
2.2%
1 1
 
2.2%
2 1
 
2.2%
6 1
 
2.2%
9 3
6.7%
11 1
 
2.2%
15 1
 
2.2%
16 2
4.4%
17 2
4.4%
18 1
 
2.2%
ValueCountFrequency (%)
3003 1
2.2%
2430 1
2.2%
2001 1
2.2%
1298 1
2.2%
919 1
2.2%
696 1
2.2%
454 1
2.2%
258 1
2.2%
231 1
2.2%
209 1
2.2%

생활정도_상류
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.088889
Minimum0
Maximum121
Zeros13
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T06:53:39.645925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile56.4
Maximum121
Range121
Interquartile range (IQR)6

Descriptive statistics

Standard deviation23.247504
Coefficient of variation (CV)2.0964683
Kurtosis11.44705
Mean11.088889
Median Absolute Deviation (MAD)2
Skewness3.1770638
Sum499
Variance540.44646
MonotonicityNot monotonic
2023-12-13T06:53:39.805855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 13
28.9%
1 7
15.6%
3 5
 
11.1%
5 4
 
8.9%
2 4
 
8.9%
10 2
 
4.4%
21 1
 
2.2%
40 1
 
2.2%
46 1
 
2.2%
67 1
 
2.2%
Other values (6) 6
13.3%
ValueCountFrequency (%)
0 13
28.9%
1 7
15.6%
2 4
 
8.9%
3 5
 
11.1%
5 4
 
8.9%
6 1
 
2.2%
10 2
 
4.4%
11 1
 
2.2%
16 1
 
2.2%
21 1
 
2.2%
ValueCountFrequency (%)
121 1
2.2%
67 1
2.2%
59 1
2.2%
46 1
2.2%
42 1
2.2%
40 1
2.2%
21 1
2.2%
16 1
2.2%
11 1
2.2%
10 2
4.4%

생활정도_미상
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.75556
Minimum1
Maximum1220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T06:53:39.985937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q19
median28
Q376
95-th percentile830
Maximum1220
Range1219
Interquartile range (IQR)67

Descriptive statistics

Standard deviation281.33021
Coefficient of variation (CV)2.0130163
Kurtosis6.4428962
Mean139.75556
Median Absolute Deviation (MAD)23
Skewness2.6434417
Sum6289
Variance79146.689
MonotonicityNot monotonic
2023-12-13T06:53:40.142833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
3 4
 
8.9%
28 3
 
6.7%
5 3
 
6.7%
15 2
 
4.4%
17 2
 
4.4%
210 2
 
4.4%
36 1
 
2.2%
75 1
 
2.2%
441 1
 
2.2%
40 1
 
2.2%
Other values (25) 25
55.6%
ValueCountFrequency (%)
1 1
 
2.2%
2 1
 
2.2%
3 4
8.9%
4 1
 
2.2%
5 3
6.7%
7 1
 
2.2%
9 1
 
2.2%
10 1
 
2.2%
12 1
 
2.2%
14 1
 
2.2%
ValueCountFrequency (%)
1220 1
2.2%
1001 1
2.2%
880 1
2.2%
630 1
2.2%
578 1
2.2%
441 1
2.2%
210 2
4.4%
188 1
2.2%
93 1
2.2%
92 1
2.2%

유배우자
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.73333
Minimum0
Maximum2142
Zeros3
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T06:53:40.306507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q112
median32
Q3110
95-th percentile1117.2
Maximum2142
Range2142
Interquartile range (IQR)98

Descriptive statistics

Standard deviation408.3252
Coefficient of variation (CV)2.2223796
Kurtosis13.119999
Mean183.73333
Median Absolute Deviation (MAD)24
Skewness3.4860876
Sum8268
Variance166729.47
MonotonicityNot monotonic
2023-12-13T06:53:40.503339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
8 4
 
8.9%
0 3
 
6.7%
24 3
 
6.7%
33 2
 
4.4%
25 2
 
4.4%
42 1
 
2.2%
63 1
 
2.2%
317 1
 
2.2%
474 1
 
2.2%
68 1
 
2.2%
Other values (26) 26
57.8%
ValueCountFrequency (%)
0 3
6.7%
1 1
 
2.2%
2 1
 
2.2%
8 4
8.9%
9 1
 
2.2%
10 1
 
2.2%
12 1
 
2.2%
14 1
 
2.2%
15 1
 
2.2%
18 1
 
2.2%
ValueCountFrequency (%)
2142 1
2.2%
1280 1
2.2%
1230 1
2.2%
666 1
2.2%
474 1
2.2%
317 1
2.2%
306 1
2.2%
302 1
2.2%
226 1
2.2%
194 1
2.2%

동거
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8666667
Minimum0
Maximum94
Zeros14
Zeros (%)31.1%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T06:53:40.623244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile45.8
Maximum94
Range94
Interquartile range (IQR)6

Descriptive statistics

Standard deviation20.027935
Coefficient of variation (CV)2.0298583
Kurtosis8.8058586
Mean9.8666667
Median Absolute Deviation (MAD)2
Skewness2.9299249
Sum444
Variance401.11818
MonotonicityNot monotonic
2023-12-13T06:53:40.744692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 14
31.1%
1 6
13.3%
3 6
13.3%
2 3
 
6.7%
13 2
 
4.4%
6 2
 
4.4%
4 2
 
4.4%
11 2
 
4.4%
35 2
 
4.4%
5 1
 
2.2%
Other values (5) 5
 
11.1%
ValueCountFrequency (%)
0 14
31.1%
1 6
13.3%
2 3
 
6.7%
3 6
13.3%
4 2
 
4.4%
5 1
 
2.2%
6 2
 
4.4%
11 2
 
4.4%
12 1
 
2.2%
13 2
 
4.4%
ValueCountFrequency (%)
94 1
2.2%
77 1
2.2%
47 1
2.2%
41 1
2.2%
35 2
4.4%
13 2
4.4%
12 1
2.2%
11 2
4.4%
6 2
4.4%
5 1
2.2%

이혼
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)57.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.555556
Minimum0
Maximum566
Zeros6
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T06:53:40.897443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q322
95-th percentile251.6
Maximum566
Range566
Interquartile range (IQR)20

Descriptive statistics

Standard deviation107.74941
Coefficient of variation (CV)2.1743154
Kurtosis11.791506
Mean49.555556
Median Absolute Deviation (MAD)5
Skewness3.1900405
Sum2230
Variance11609.934
MonotonicityNot monotonic
2023-12-13T06:53:41.044454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2 8
17.8%
0 6
 
13.3%
1 4
 
8.9%
22 2
 
4.4%
3 2
 
4.4%
10 2
 
4.4%
6 2
 
4.4%
266 1
 
2.2%
209 1
 
2.2%
566 1
 
2.2%
Other values (16) 16
35.6%
ValueCountFrequency (%)
0 6
13.3%
1 4
8.9%
2 8
17.8%
3 2
 
4.4%
4 1
 
2.2%
5 1
 
2.2%
6 2
 
4.4%
7 1
 
2.2%
9 1
 
2.2%
10 2
 
4.4%
ValueCountFrequency (%)
566 1
2.2%
266 1
2.2%
256 1
2.2%
234 1
2.2%
209 1
2.2%
202 1
2.2%
117 1
2.2%
53 1
2.2%
50 1
2.2%
48 1
2.2%

사별
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4444444
Minimum0
Maximum163
Zeros17
Zeros (%)37.8%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T06:53:41.191406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile24.2
Maximum163
Range163
Interquartile range (IQR)4

Descriptive statistics

Standard deviation24.713408
Coefficient of variation (CV)3.3197115
Kurtosis37.709616
Mean7.4444444
Median Absolute Deviation (MAD)1
Skewness5.9496582
Sum335
Variance610.75253
MonotonicityNot monotonic
2023-12-13T06:53:41.332224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 17
37.8%
2 7
15.6%
1 7
15.6%
11 2
 
4.4%
4 2
 
4.4%
3 2
 
4.4%
31 1
 
2.2%
5 1
 
2.2%
7 1
 
2.2%
9 1
 
2.2%
Other values (4) 4
 
8.9%
ValueCountFrequency (%)
0 17
37.8%
1 7
15.6%
2 7
15.6%
3 2
 
4.4%
4 2
 
4.4%
5 1
 
2.2%
7 1
 
2.2%
9 1
 
2.2%
11 2
 
4.4%
17 1
 
2.2%
ValueCountFrequency (%)
163 1
2.2%
31 1
2.2%
25 1
2.2%
21 1
2.2%
17 1
2.2%
11 2
4.4%
9 1
2.2%
7 1
2.2%
5 1
2.2%
4 2
4.4%

혼인관계_미상
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.95556
Minimum1
Maximum1222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T06:53:41.505453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q19
median28
Q376
95-th percentile828.2
Maximum1222
Range1221
Interquartile range (IQR)67

Descriptive statistics

Standard deviation281.45022
Coefficient of variation (CV)2.0109971
Kurtosis6.464714
Mean139.95556
Median Absolute Deviation (MAD)23
Skewness2.6453126
Sum6298
Variance79214.225
MonotonicityNot monotonic
2023-12-13T06:53:41.697771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3 5
 
11.1%
28 3
 
6.7%
5 2
 
4.4%
15 2
 
4.4%
17 2
 
4.4%
7 1
 
2.2%
75 1
 
2.2%
213 1
 
2.2%
441 1
 
2.2%
41 1
 
2.2%
Other values (26) 26
57.8%
ValueCountFrequency (%)
1 1
 
2.2%
2 1
 
2.2%
3 5
11.1%
5 2
 
4.4%
6 1
 
2.2%
7 1
 
2.2%
9 1
 
2.2%
10 1
 
2.2%
12 1
 
2.2%
14 1
 
2.2%
ValueCountFrequency (%)
1222 1
2.2%
1003 1
2.2%
878 1
2.2%
629 1
2.2%
577 1
2.2%
441 1
2.2%
213 1
2.2%
211 1
2.2%
188 1
2.2%
95 1
2.2%

실(양)부모
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean252.91111
Minimum0
Maximum3399
Zeros2
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T06:53:42.182033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median25
Q365
95-th percentile1842
Maximum3399
Range3399
Interquartile range (IQR)58

Descriptive statistics

Standard deviation679.37872
Coefficient of variation (CV)2.6862352
Kurtosis12.6334
Mean252.91111
Median Absolute Deviation (MAD)21
Skewness3.5395373
Sum11381
Variance461555.45
MonotonicityNot monotonic
2023-12-13T06:53:42.329313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1 3
 
6.7%
3 2
 
4.4%
25 2
 
4.4%
7 2
 
4.4%
37 2
 
4.4%
19 2
 
4.4%
5 2
 
4.4%
9 2
 
4.4%
0 2
 
4.4%
53 1
 
2.2%
Other values (25) 25
55.6%
ValueCountFrequency (%)
0 2
4.4%
1 3
6.7%
3 2
4.4%
5 2
4.4%
6 1
 
2.2%
7 2
4.4%
9 2
4.4%
10 1
 
2.2%
12 1
 
2.2%
13 1
 
2.2%
ValueCountFrequency (%)
3399 1
2.2%
2355 1
2.2%
2109 1
2.2%
774 1
2.2%
769 1
2.2%
495 1
2.2%
344 1
2.2%
184 1
2.2%
134 1
2.2%
68 1
2.2%

계부모
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8
Minimum0
Maximum11
Zeros36
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T06:53:42.459827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6.2
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.2623399
Coefficient of variation (CV)2.8279249
Kurtosis11.731794
Mean0.8
Median Absolute Deviation (MAD)0
Skewness3.4271951
Sum36
Variance5.1181818
MonotonicityNot monotonic
2023-12-13T06:53:42.589430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 36
80.0%
1 3
 
6.7%
2 2
 
4.4%
8 1
 
2.2%
11 1
 
2.2%
7 1
 
2.2%
3 1
 
2.2%
ValueCountFrequency (%)
0 36
80.0%
1 3
 
6.7%
2 2
 
4.4%
3 1
 
2.2%
7 1
 
2.2%
8 1
 
2.2%
11 1
 
2.2%
ValueCountFrequency (%)
11 1
 
2.2%
8 1
 
2.2%
7 1
 
2.2%
3 1
 
2.2%
2 2
 
4.4%
1 3
 
6.7%
0 36
80.0%

실부계모
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8222222
Minimum0
Maximum33
Zeros33
Zeros (%)73.3%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T06:53:42.705419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile8.8
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.6338623
Coefficient of variation (CV)3.0917537
Kurtosis22.714913
Mean1.8222222
Median Absolute Deviation (MAD)0
Skewness4.546306
Sum82
Variance31.740404
MonotonicityNot monotonic
2023-12-13T06:53:42.826890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 33
73.3%
1 3
 
6.7%
4 3
 
6.7%
2 2
 
4.4%
3 1
 
2.2%
10 1
 
2.2%
17 1
 
2.2%
33 1
 
2.2%
ValueCountFrequency (%)
0 33
73.3%
1 3
 
6.7%
2 2
 
4.4%
3 1
 
2.2%
4 3
 
6.7%
10 1
 
2.2%
17 1
 
2.2%
33 1
 
2.2%
ValueCountFrequency (%)
33 1
 
2.2%
17 1
 
2.2%
10 1
 
2.2%
4 3
 
6.7%
3 1
 
2.2%
2 2
 
4.4%
1 3
 
6.7%
0 33
73.3%

실부무모
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.644444
Minimum0
Maximum205
Zeros19
Zeros (%)42.2%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T06:53:42.952390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile86.8
Maximum205
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation42.274082
Coefficient of variation (CV)2.7021785
Kurtosis12.545262
Mean15.644444
Median Absolute Deviation (MAD)1
Skewness3.5235475
Sum704
Variance1787.098
MonotonicityNot monotonic
2023-12-13T06:53:43.071804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 19
42.2%
1 5
 
11.1%
2 5
 
11.1%
3 4
 
8.9%
5 2
 
4.4%
4 1
 
2.2%
24 1
 
2.2%
88 1
 
2.2%
8 1
 
2.2%
28 1
 
2.2%
Other values (5) 5
 
11.1%
ValueCountFrequency (%)
0 19
42.2%
1 5
 
11.1%
2 5
 
11.1%
3 4
 
8.9%
4 1
 
2.2%
5 2
 
4.4%
8 1
 
2.2%
13 1
 
2.2%
24 1
 
2.2%
28 1
 
2.2%
ValueCountFrequency (%)
205 1
2.2%
172 1
2.2%
88 1
2.2%
82 1
2.2%
43 1
2.2%
28 1
2.2%
24 1
2.2%
13 1
2.2%
8 1
2.2%
5 2
4.4%

실모계부
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3111111
Minimum0
Maximum30
Zeros29
Zeros (%)64.4%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T06:53:43.217165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile9.8
Maximum30
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.3203216
Coefficient of variation (CV)2.7347545
Kurtosis14.76601
Mean2.3111111
Median Absolute Deviation (MAD)0
Skewness3.8281302
Sum104
Variance39.946465
MonotonicityNot monotonic
2023-12-13T06:53:43.356325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 29
64.4%
1 6
 
13.3%
4 3
 
6.7%
3 2
 
4.4%
10 1
 
2.2%
2 1
 
2.2%
30 1
 
2.2%
29 1
 
2.2%
9 1
 
2.2%
ValueCountFrequency (%)
0 29
64.4%
1 6
 
13.3%
2 1
 
2.2%
3 2
 
4.4%
4 3
 
6.7%
9 1
 
2.2%
10 1
 
2.2%
29 1
 
2.2%
30 1
 
2.2%
ValueCountFrequency (%)
30 1
 
2.2%
29 1
 
2.2%
10 1
 
2.2%
9 1
 
2.2%
4 3
 
6.7%
3 2
 
4.4%
2 1
 
2.2%
1 6
 
13.3%
0 29
64.4%

실모무부
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.644444
Minimum0
Maximum515
Zeros8
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T06:53:43.502100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q314
95-th percentile245.2
Maximum515
Range515
Interquartile range (IQR)13

Descriptive statistics

Standard deviation98.431369
Coefficient of variation (CV)2.4217669
Kurtosis13.055928
Mean40.644444
Median Absolute Deviation (MAD)5
Skewness3.4549599
Sum1829
Variance9688.7343
MonotonicityNot monotonic
2023-12-13T06:53:43.648294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 8
17.8%
1 5
 
11.1%
4 4
 
8.9%
2 4
 
8.9%
7 3
 
6.7%
10 2
 
4.4%
9 2
 
4.4%
5 2
 
4.4%
14 1
 
2.2%
80 1
 
2.2%
Other values (13) 13
28.9%
ValueCountFrequency (%)
0 8
17.8%
1 5
11.1%
2 4
8.9%
3 1
 
2.2%
4 4
8.9%
5 2
 
4.4%
6 1
 
2.2%
7 3
 
6.7%
9 2
 
4.4%
10 2
 
4.4%
ValueCountFrequency (%)
515 1
2.2%
307 1
2.2%
265 1
2.2%
166 1
2.2%
140 1
2.2%
112 1
2.2%
80 1
2.2%
50 1
2.2%
27 1
2.2%
19 1
2.2%

계부무모
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size492.0 B
0
42 
1
 
2
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 42
93.3%
1 2
 
4.4%
3 1
 
2.2%

Length

2023-12-13T06:53:43.775156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:53:43.898905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 42
93.3%
1 2
 
4.4%
3 1
 
2.2%

계모무부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size492.0 B
0
38 
1
2
 
2
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 38
84.4%
1 4
 
8.9%
2 2
 
4.4%
3 1
 
2.2%

Length

2023-12-13T06:53:44.052403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:53:44.191999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 38
84.4%
1 4
 
8.9%
2 2
 
4.4%
3 1
 
2.2%

무부모
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.422222
Minimum0
Maximum412
Zeros13
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T06:53:44.319350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q312
95-th percentile107.8
Maximum412
Range412
Interquartile range (IQR)12

Descriptive statistics

Standard deviation66.109925
Coefficient of variation (CV)2.82253
Kurtosis28.115892
Mean23.422222
Median Absolute Deviation (MAD)2
Skewness4.9640851
Sum1054
Variance4370.5222
MonotonicityNot monotonic
2023-12-13T06:53:44.440363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 13
28.9%
1 8
17.8%
2 6
13.3%
8 2
 
4.4%
19 2
 
4.4%
3 2
 
4.4%
12 1
 
2.2%
9 1
 
2.2%
79 1
 
2.2%
412 1
 
2.2%
Other values (8) 8
17.8%
ValueCountFrequency (%)
0 13
28.9%
1 8
17.8%
2 6
13.3%
3 2
 
4.4%
4 1
 
2.2%
8 2
 
4.4%
9 1
 
2.2%
12 1
 
2.2%
17 1
 
2.2%
18 1
 
2.2%
ValueCountFrequency (%)
412 1
2.2%
118 1
2.2%
115 1
2.2%
79 1
2.2%
70 1
2.2%
61 1
2.2%
59 1
2.2%
19 2
4.4%
18 1
2.2%
17 1
2.2%

미혼자부모관계_미상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2444444
Minimum0
Maximum16
Zeros35
Zeros (%)77.8%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T06:53:44.578067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11.4
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.650377
Coefficient of variation (CV)2.9333387
Kurtosis9.046692
Mean1.2444444
Median Absolute Deviation (MAD)0
Skewness3.1686828
Sum56
Variance13.325253
MonotonicityNot monotonic
2023-12-13T06:53:44.707459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 35
77.8%
1 6
 
13.3%
12 1
 
2.2%
13 1
 
2.2%
16 1
 
2.2%
9 1
 
2.2%
ValueCountFrequency (%)
0 35
77.8%
1 6
 
13.3%
9 1
 
2.2%
12 1
 
2.2%
13 1
 
2.2%
16 1
 
2.2%
ValueCountFrequency (%)
16 1
 
2.2%
13 1
 
2.2%
12 1
 
2.2%
9 1
 
2.2%
1 6
 
13.3%
0 35
77.8%

Interactions

2023-12-13T06:53:36.040949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:09.154541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:11.193507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:12.802934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:14.612535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:16.315229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:18.100873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:19.725592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:21.163893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:22.455089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:23.983730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:25.498954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:26.959952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:28.464195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:29.961506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:31.737089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:33.811067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:36.174942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:09.262520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:11.291401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:12.909312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:14.724811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:16.399331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:18.199517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:19.848211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-13T06:53:10.469895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:12.411495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:14.174590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:15.921003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:17.725273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:19.412339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:20.821346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:22.167655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:23.649583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:25.157637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:26.626473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:27.912447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:29.621536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:31.267480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:33.314128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:35.651208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:37.622364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:10.574212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:12.508441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:14.276596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:16.014284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:17.813007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:19.494583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:20.899624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:22.242396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:23.727523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:25.243449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:26.715075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:28.249336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:29.707008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:31.382321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:33.434855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:35.764024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:37.734170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:10.972213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:12.597103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:14.382759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:16.113893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:17.911011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:19.562860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:20.993836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:22.313486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:23.811558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:25.330957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:26.801599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:28.314082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:29.799377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:31.547387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:33.571004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:35.854499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:37.835211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:11.075548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:12.684307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:14.486125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:16.203394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:17.996036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:19.638862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:21.080347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:22.379954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:23.888781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:25.407660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:26.878667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:28.381171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:29.873899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:31.632488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:33.685923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:35.925912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:53:44.825707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
직업별생활정도_하류생활정도_중류생활정도_상류생활정도_미상유배우자동거이혼사별혼인관계_미상실(양)부모계부모실부계모실부무모실모계부실모무부계부무모계모무부무부모미혼자부모관계_미상
직업별1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
생활정도_하류1.0001.0000.9890.9600.9950.9720.9810.8430.9980.9950.8840.9940.9660.9940.8860.9940.9930.8550.9780.931
생활정도_중류1.0000.9891.0000.9570.9990.9630.9260.8721.0000.9991.0001.0000.9600.9790.9900.9481.0000.9850.9941.000
생활정도_상류1.0000.9600.9571.0000.9340.9440.9120.5770.8930.9340.8550.9750.8390.9540.8100.9080.9690.6990.7210.867
생활정도_미상1.0000.9950.9990.9341.0000.9940.9420.9141.0001.0000.9601.0000.9690.9890.9910.9681.0000.9880.9971.000
유배우자1.0000.9720.9630.9440.9941.0000.9710.8250.9750.9940.7970.9780.8130.9640.8130.9690.8470.7290.8540.849
동거1.0000.9810.9260.9120.9420.9711.0000.8300.9990.9420.7490.9850.8860.9600.8570.9700.9590.8130.9030.849
이혼1.0000.8430.8720.5770.9140.8250.8301.0000.8990.9140.9300.8150.9520.8600.6000.8380.7330.6760.8020.930
사별1.0000.9981.0000.8931.0000.9750.9990.8991.0001.0000.7581.0000.8340.9990.7030.9990.9500.6870.8340.818
혼인관계_미상1.0000.9950.9990.9341.0000.9940.9420.9141.0001.0000.9601.0000.9690.9890.9910.9681.0000.9880.9971.000
실(양)부모1.0000.8841.0000.8550.9600.7970.7490.9300.7580.9601.0000.9020.9790.8750.7080.8160.7550.6870.6940.973
계부모1.0000.9941.0000.9751.0000.9780.9850.8151.0001.0000.9021.0000.9430.9940.9630.9780.9960.9070.9251.000
실부계모1.0000.9660.9600.8390.9690.8130.8860.9520.8340.9690.9790.9431.0000.9170.7720.8850.8210.7760.8630.995
실부무모1.0000.9940.9790.9540.9890.9640.9600.8600.9990.9890.8750.9940.9171.0000.8900.9850.9960.9230.9470.945
실모계부1.0000.8860.9900.8100.9910.8130.8570.6000.7030.9910.7080.9630.7720.8901.0000.8140.5970.8710.9110.827
실모무부1.0000.9940.9480.9080.9680.9690.9700.8380.9990.9680.8160.9780.8850.9850.8141.0000.9830.8531.0000.848
계부무모1.0000.9931.0000.9691.0000.8470.9590.7330.9501.0000.7550.9960.8210.9960.5970.9831.0000.7230.7890.820
계모무부1.0000.8550.9850.6990.9880.7290.8130.6760.6870.9880.6870.9070.7760.9230.8710.8530.7231.0000.9500.828
무부모1.0000.9780.9940.7210.9970.8540.9030.8020.8340.9970.6940.9250.8630.9470.9111.0000.7890.9501.0000.779
미혼자부모관계_미상1.0000.9311.0000.8671.0000.8490.8490.9300.8181.0000.9731.0000.9950.9450.8270.8480.8200.8280.7791.000
2023-12-13T06:53:45.038382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계모무부계부무모
계모무부1.0000.756
계부무모0.7561.000
2023-12-13T06:53:45.179040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생활정도_하류생활정도_중류생활정도_상류생활정도_미상유배우자동거이혼사별혼인관계_미상실(양)부모계부모실부계모실부무모실모계부실모무부무부모미혼자부모관계_미상계부무모계모무부
생활정도_하류1.0000.8980.5140.9180.8190.8820.8540.7530.9200.8960.6640.6370.8430.7060.9230.9220.5470.8640.702
생활정도_중류0.8981.0000.6640.9380.8880.8210.7980.7920.9380.8950.6250.6170.8150.6030.9160.8070.5390.9390.793
생활정도_상류0.5140.6641.0000.6220.6740.5000.4700.4960.6230.5230.5360.4770.3890.4090.5230.4720.4850.7560.515
생활정도_미상0.9180.9380.6221.0000.8690.8760.8670.7961.0000.8540.6490.6440.7710.6840.8810.8350.5130.9390.806
유배우자0.8190.8880.6740.8691.0000.8150.8940.8650.8710.6970.5170.5060.6500.4820.7650.7810.4600.5190.548
동거0.8820.8210.5000.8760.8151.0000.8550.8150.8750.7470.5990.6280.7150.6720.8340.8970.4830.7250.648
이혼0.8540.7980.4700.8670.8940.8551.0000.8340.8690.7030.4540.5120.6580.5440.7540.8110.4170.7090.601
사별0.7530.7920.4960.7960.8650.8150.8341.0000.7970.6540.5100.4810.5930.4820.7330.7200.3890.7260.711
혼인관계_미상0.9200.9380.6231.0000.8710.8750.8690.7971.0000.8550.6490.6440.7720.6860.8810.8350.5110.9390.806
실(양)부모0.8960.8950.5230.8540.6970.7470.7030.6540.8551.0000.6720.6230.8590.6780.9280.8000.5270.7400.613
계부모0.6640.6250.5360.6490.5170.5990.4540.5100.6490.6721.0000.7990.6880.6810.6800.6680.6210.8900.772
실부계모0.6370.6170.4770.6440.5060.6280.5120.4810.6440.6230.7991.0000.6320.7080.6660.6290.5140.8280.719
실부무모0.8430.8150.3890.7710.6500.7150.6580.5930.7720.8590.6880.6321.0000.6480.9100.7810.4590.8760.777
실모계부0.7060.6030.4090.6840.4820.6720.5440.4820.6860.6780.6810.7080.6481.0000.7250.7300.5190.6140.607
실모무부0.9230.9160.5230.8810.7650.8340.7540.7330.8810.9280.6800.6660.9100.7251.0000.8820.5100.8090.700
무부모0.9220.8070.4720.8350.7810.8970.8110.7200.8350.8000.6680.6290.7810.7300.8821.0000.5600.8380.699
미혼자부모관계_미상0.5470.5390.4850.5130.4600.4830.4170.3890.5110.5270.6210.5140.4590.5190.5100.5601.0000.8270.787
계부무모0.8640.9390.7560.9390.5190.7250.7090.7260.9390.7400.8900.8280.8760.6140.8090.8380.8271.0000.756
계모무부0.7020.7930.5150.8060.5480.6480.6010.7110.8060.6130.7720.7190.7770.6070.7000.6990.7870.7561.000

Missing values

2023-12-13T06:53:37.991881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:53:38.267573image/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·수산업177143576194135311762402111400120
1제조업47411017612621723000020011
2건설업1121241193140648295250031120080
3도·소매업2228317321301712000120020
4무역업81611014030107000010000
5요식업62956527861825352000040020
6숙박업1115251802067000000000
7유흥업38393322451013226014170010
8금융업4933901036000000000
9부동산업38605304741113030002060020
직업별생활정도_하류생활정도_중류생활정도_상류생활정도_미상유배우자동거이혼사별혼인관계_미상실(양)부모계부모실부계모실부무모실모계부실모무부계부무모계모무부무부모미혼자부모관계_미상
35예술인3867236333423653002090010
36기타전문직3244542121030212504211344121305001181
37공무원492581692226116294610030100020
38군인0004000031000000000
39학생1505243067578283105773399111717230265126113
40주부161802825171280000000000
41전경·의경416020000219000010000
42공익요원294701500101565005050000
43무직자3636200146122012809456616312222355733205295153341216
44미상10289194088066635209218787693443914002799