Overview

Dataset statistics

Number of variables12
Number of observations105
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 KiB
Average record size in memory108.3 B

Variable types

Text1
Numeric8
Categorical3

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 고령범죄자에 관한 공범관계 통계임.
Author대검찰청
URLhttps://www.data.go.kr/data/15084673/fileData.do

Alerts

단독범 is highly overall correlated with 직장동료 and 6 other fieldsHigh correlation
직장동료 is highly overall correlated with 단독범 and 8 other fieldsHigh correlation
친인척 is highly overall correlated with 단독범 and 8 other fieldsHigh correlation
동네친구 is highly overall correlated with 단독범 and 7 other fieldsHigh correlation
고향친구 is highly overall correlated with 직장동료 and 5 other fieldsHigh correlation
애인 is highly overall correlated with 친인척 and 4 other fieldsHigh correlation
기타 is highly overall correlated with 단독범 and 8 other fieldsHigh correlation
미상 is highly overall correlated with 단독범 and 7 other fieldsHigh correlation
학교동창 is highly overall correlated with 단독범 and 7 other fieldsHigh correlation
교도소_소년원동료 is highly overall correlated with 단독범 and 8 other fieldsHigh correlation
군동료 is highly overall correlated with 직장동료 and 3 other fieldsHigh correlation
학교동창 is highly imbalanced (73.4%)Imbalance
교도소_소년원동료 is highly imbalanced (88.4%)Imbalance
군동료 is highly imbalanced (88.4%)Imbalance
범죄분류 has unique valuesUnique
직장동료 has 38 (36.2%) zerosZeros
친인척 has 42 (40.0%) zerosZeros
동네친구 has 60 (57.1%) zerosZeros
고향친구 has 87 (82.9%) zerosZeros
애인 has 90 (85.7%) zerosZeros
기타 has 13 (12.4%) zerosZeros
미상 has 6 (5.7%) zerosZeros

Reproduction

Analysis started2023-12-12 02:18:57.051272
Analysis finished2023-12-12 02:19:05.154946
Duration8.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct105
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size972.0 B
2023-12-12T11:19:05.392064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length23
Mean length7.352381
Min length2

Characters and Unicode

Total characters772
Distinct characters180
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique105 ?
Unique (%)100.0%

Sample

1st row절도
2nd row장물
3rd row사기
4th row횡령
5th row배임
ValueCountFrequency (%)
관한법률 11
 
6.1%
및 9
 
5.0%
관리에 3
 
1.7%
과실치사상 2
 
1.1%
등 2
 
1.1%
보호법 2
 
1.1%
관한죄 2
 
1.1%
운수사업법 2
 
1.1%
보장법 2
 
1.1%
성보호에 2
 
1.1%
Other values (140) 142
79.3%
2023-12-12T11:19:05.855666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
74
 
9.6%
법 59
 
7.6%
관 27
 
3.5%
한 17
 
2.2%
에 16
 
2.1%
보 16
 
2.1%
리 14
 
1.8%
률 13
 
1.7%
의 11
 
1.4%
수 11
 
1.4%
Other values (170) 514
66.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 684
88.6%
Space Separator 74
 
9.6%
Other Punctuation 6
 
0.8%
Close Punctuation 4
 
0.5%
Open Punctuation 4
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
법 59
 
8.6%
관 27
 
3.9%
한 17
 
2.5%
에 16
 
2.3%
보 16
 
2.3%
리 14
 
2.0%
률 13
 
1.9%
의 11
 
1.6%
수 11
 
1.6%
업 11
 
1.6%
Other values (165) 489
71.5%
Other Punctuation
ValueCountFrequency (%)
· 4
66.7%
, 2
33.3%
Space Separator
ValueCountFrequency (%)
74
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 684
88.6%
Common 88
 
11.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
법 59
 
8.6%
관 27
 
3.9%
한 17
 
2.5%
에 16
 
2.3%
보 16
 
2.3%
리 14
 
2.0%
률 13
 
1.9%
의 11
 
1.6%
수 11
 
1.6%
업 11
 
1.6%
Other values (165) 489
71.5%
Common
ValueCountFrequency (%)
74
84.1%
) 4
 
4.5%
· 4
 
4.5%
( 4
 
4.5%
, 2
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 684
88.6%
ASCII 84
 
10.9%
None 4
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
74
88.1%
) 4
 
4.8%
( 4
 
4.8%
, 2
 
2.4%
Hangul
ValueCountFrequency (%)
법 59
 
8.6%
관 27
 
3.9%
한 17
 
2.5%
에 16
 
2.3%
보 16
 
2.3%
리 14
 
2.0%
률 13
 
1.9%
의 11
 
1.6%
수 11
 
1.6%
업 11
 
1.6%
Other values (165) 489
71.5%
None
ValueCountFrequency (%)
· 4
100.0%

단독범
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean897.42857
Minimum2
Maximum21756
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T11:19:06.064512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q125
median112
Q3395
95-th percentile4290.8
Maximum21756
Range21754
Interquartile range (IQR)370

Descriptive statistics

Standard deviation2823.1981
Coefficient of variation (CV)3.145875
Kurtosis32.194262
Mean897.42857
Median Absolute Deviation (MAD)103
Skewness5.3027818
Sum94230
Variance7970447.7
MonotonicityNot monotonic
2023-12-12T11:19:06.284863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 5
 
4.8%
2 4
 
3.8%
3 3
 
2.9%
7 3
 
2.9%
83 2
 
1.9%
139 2
 
1.9%
101 2
 
1.9%
25 2
 
1.9%
19 2
 
1.9%
43 2
 
1.9%
Other values (77) 78
74.3%
ValueCountFrequency (%)
2 4
3.8%
3 3
2.9%
4 1
 
1.0%
5 5
4.8%
7 3
2.9%
9 1
 
1.0%
13 1
 
1.0%
14 1
 
1.0%
15 1
 
1.0%
17 1
 
1.0%
ValueCountFrequency (%)
21756 1
1.0%
11506 1
1.0%
10715 1
1.0%
10447 1
1.0%
4702 1
1.0%
4443 1
1.0%
3682 1
1.0%
2404 1
1.0%
2379 1
1.0%
1921 1
1.0%

학교동창
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size972.0 B
0
95 
2
 
4
1
 
4
4
 
1
9
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)1.9%

Sample

1st row2
2nd row0
3rd row4
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 95
90.5%
2 4
 
3.8%
1 4
 
3.8%
4 1
 
1.0%
9 1
 
1.0%

Length

2023-12-12T11:19:06.453572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:19:06.600364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 95
90.5%
2 4
 
3.8%
1 4
 
3.8%
4 1
 
1.0%
9 1
 
1.0%

교도소_소년원동료
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size972.0 B
0
102 
9
 
1
3
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique3 ?
Unique (%)2.9%

Sample

1st row9
2nd row0
3rd row3
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 102
97.1%
9 1
 
1.0%
3 1
 
1.0%
2 1
 
1.0%

Length

2023-12-12T11:19:06.739365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:19:06.861723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 102
97.1%
9 1
 
1.0%
3 1
 
1.0%
2 1
 
1.0%

직장동료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0380952
Minimum0
Maximum196
Zeros38
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T11:19:07.013424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile33.2
Maximum196
Range196
Interquartile range (IQR)5

Descriptive statistics

Standard deviation26.995343
Coefficient of variation (CV)2.9868398
Kurtosis30.618112
Mean9.0380952
Median Absolute Deviation (MAD)1
Skewness5.2645001
Sum949
Variance728.74853
MonotonicityNot monotonic
2023-12-12T11:19:07.161511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 38
36.2%
1 18
17.1%
2 14
 
13.3%
4 4
 
3.8%
3 4
 
3.8%
7 3
 
2.9%
5 3
 
2.9%
14 2
 
1.9%
10 2
 
1.9%
26 2
 
1.9%
Other values (14) 15
 
14.3%
ValueCountFrequency (%)
0 38
36.2%
1 18
17.1%
2 14
 
13.3%
3 4
 
3.8%
4 4
 
3.8%
5 3
 
2.9%
7 3
 
2.9%
8 1
 
1.0%
9 1
 
1.0%
10 2
 
1.9%
ValueCountFrequency (%)
196 1
1.0%
156 1
1.0%
97 1
1.0%
55 1
1.0%
52 1
1.0%
34 1
1.0%
30 1
1.0%
28 1
1.0%
26 2
1.9%
19 1
1.0%

친인척
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.361905
Minimum0
Maximum176
Zeros42
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T11:19:07.315892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37
95-th percentile53.2
Maximum176
Range176
Interquartile range (IQR)7

Descriptive statistics

Standard deviation31.231494
Coefficient of variation (CV)2.7487903
Kurtosis16.371566
Mean11.361905
Median Absolute Deviation (MAD)1
Skewness4.0127297
Sum1193
Variance975.40623
MonotonicityNot monotonic
2023-12-12T11:19:07.446719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 42
40.0%
1 17
16.2%
2 9
 
8.6%
7 4
 
3.8%
8 4
 
3.8%
10 3
 
2.9%
5 3
 
2.9%
3 2
 
1.9%
6 2
 
1.9%
4 2
 
1.9%
Other values (16) 17
16.2%
ValueCountFrequency (%)
0 42
40.0%
1 17
16.2%
2 9
 
8.6%
3 2
 
1.9%
4 2
 
1.9%
5 3
 
2.9%
6 2
 
1.9%
7 4
 
3.8%
8 4
 
3.8%
9 1
 
1.0%
ValueCountFrequency (%)
176 1
1.0%
158 2
1.9%
106 1
1.0%
103 1
1.0%
54 1
1.0%
50 1
1.0%
37 1
1.0%
31 1
1.0%
30 1
1.0%
28 1
1.0%

군동료
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size972.0 B
0
102 
4
 
1
2
 
1
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique3 ?
Unique (%)2.9%

Sample

1st row0
2nd row0
3rd row0
4th row4
5th row0

Common Values

ValueCountFrequency (%)
0 102
97.1%
4 1
 
1.0%
2 1
 
1.0%
1 1
 
1.0%

Length

2023-12-12T11:19:07.597038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:19:07.719704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 102
97.1%
4 1
 
1.0%
2 1
 
1.0%
1 1
 
1.0%

동네친구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9047619
Minimum0
Maximum546
Zeros60
Zeros (%)57.1%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T11:19:07.837311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile28.8
Maximum546
Range546
Interquartile range (IQR)2

Descriptive statistics

Standard deviation55.616566
Coefficient of variation (CV)5.615134
Kurtosis85.368874
Mean9.9047619
Median Absolute Deviation (MAD)0
Skewness8.9686617
Sum1040
Variance3093.2024
MonotonicityNot monotonic
2023-12-12T11:19:08.000015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 60
57.1%
1 12
 
11.4%
2 10
 
9.5%
3 4
 
3.8%
4 3
 
2.9%
5 3
 
2.9%
16 3
 
2.9%
158 1
 
1.0%
40 1
 
1.0%
28 1
 
1.0%
Other values (7) 7
 
6.7%
ValueCountFrequency (%)
0 60
57.1%
1 12
 
11.4%
2 10
 
9.5%
3 4
 
3.8%
4 3
 
2.9%
5 3
 
2.9%
6 1
 
1.0%
10 1
 
1.0%
13 1
 
1.0%
16 3
 
2.9%
ValueCountFrequency (%)
546 1
 
1.0%
158 1
 
1.0%
57 1
 
1.0%
40 1
 
1.0%
34 1
 
1.0%
29 1
 
1.0%
28 1
 
1.0%
16 3
2.9%
13 1
 
1.0%
10 1
 
1.0%

고향친구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97142857
Minimum0
Maximum39
Zeros87
Zeros (%)82.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T11:19:08.119757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.8
Maximum39
Range39
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.1935324
Coefficient of variation (CV)4.3168716
Kurtosis66.521838
Mean0.97142857
Median Absolute Deviation (MAD)0
Skewness7.6206004
Sum102
Variance17.585714
MonotonicityNot monotonic
2023-12-12T11:19:08.259034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 87
82.9%
1 6
 
5.7%
2 3
 
2.9%
6 2
 
1.9%
3 2
 
1.9%
9 1
 
1.0%
12 1
 
1.0%
5 1
 
1.0%
7 1
 
1.0%
39 1
 
1.0%
ValueCountFrequency (%)
0 87
82.9%
1 6
 
5.7%
2 3
 
2.9%
3 2
 
1.9%
5 1
 
1.0%
6 2
 
1.9%
7 1
 
1.0%
9 1
 
1.0%
12 1
 
1.0%
39 1
 
1.0%
ValueCountFrequency (%)
39 1
 
1.0%
12 1
 
1.0%
9 1
 
1.0%
7 1
 
1.0%
6 2
 
1.9%
5 1
 
1.0%
3 2
 
1.9%
2 3
 
2.9%
1 6
 
5.7%
0 87
82.9%

애인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6
Minimum0
Maximum19
Zeros90
Zeros (%)85.7%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T11:19:08.414216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.8
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.3229623
Coefficient of variation (CV)3.8716038
Kurtosis40.22479
Mean0.6
Median Absolute Deviation (MAD)0
Skewness5.8571095
Sum63
Variance5.3961538
MonotonicityNot monotonic
2023-12-12T11:19:08.554727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 90
85.7%
1 5
 
4.8%
2 4
 
3.8%
8 3
 
2.9%
19 1
 
1.0%
3 1
 
1.0%
4 1
 
1.0%
ValueCountFrequency (%)
0 90
85.7%
1 5
 
4.8%
2 4
 
3.8%
3 1
 
1.0%
4 1
 
1.0%
8 3
 
2.9%
19 1
 
1.0%
ValueCountFrequency (%)
19 1
 
1.0%
8 3
 
2.9%
4 1
 
1.0%
3 1
 
1.0%
2 4
 
3.8%
1 5
 
4.8%
0 90
85.7%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.647619
Minimum0
Maximum919
Zeros13
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T11:19:08.756576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median10
Q332
95-th percentile183.2
Maximum919
Range919
Interquartile range (IQR)30

Descriptive statistics

Standard deviation148.01137
Coefficient of variation (CV)2.8113592
Kurtosis24.621251
Mean52.647619
Median Absolute Deviation (MAD)10
Skewness4.882625
Sum5528
Variance21907.365
MonotonicityNot monotonic
2023-12-12T11:19:08.950536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 13
 
12.4%
2 7
 
6.7%
1 7
 
6.7%
3 7
 
6.7%
7 4
 
3.8%
6 4
 
3.8%
12 4
 
3.8%
10 4
 
3.8%
4 4
 
3.8%
42 3
 
2.9%
Other values (38) 48
45.7%
ValueCountFrequency (%)
0 13
12.4%
1 7
6.7%
2 7
6.7%
3 7
6.7%
4 4
 
3.8%
5 1
 
1.0%
6 4
 
3.8%
7 4
 
3.8%
8 2
 
1.9%
9 1
 
1.0%
ValueCountFrequency (%)
919 1
1.0%
840 1
1.0%
823 1
1.0%
289 1
1.0%
249 1
1.0%
184 1
1.0%
180 1
1.0%
151 1
1.0%
143 1
1.0%
108 1
1.0%

미상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct75
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.52381
Minimum0
Maximum4157
Zeros6
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T11:19:09.122811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q16
median30
Q395
95-th percentile877.8
Maximum4157
Range4157
Interquartile range (IQR)89

Descriptive statistics

Standard deviation473.83154
Coefficient of variation (CV)2.6842359
Kurtosis48.604899
Mean176.52381
Median Absolute Deviation (MAD)28
Skewness6.2373913
Sum18535
Variance224516.33
MonotonicityNot monotonic
2023-12-12T11:19:09.308068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
5.7%
1 6
 
5.7%
2 5
 
4.8%
5 3
 
2.9%
6 3
 
2.9%
3 3
 
2.9%
47 2
 
1.9%
10 2
 
1.9%
14 2
 
1.9%
66 2
 
1.9%
Other values (65) 71
67.6%
ValueCountFrequency (%)
0 6
5.7%
1 6
5.7%
2 5
4.8%
3 3
2.9%
4 1
 
1.0%
5 3
2.9%
6 3
2.9%
8 1
 
1.0%
9 1
 
1.0%
10 2
 
1.9%
ValueCountFrequency (%)
4157 1
1.0%
1376 1
1.0%
1204 1
1.0%
1021 1
1.0%
1006 1
1.0%
912 1
1.0%
741 1
1.0%
693 1
1.0%
662 1
1.0%
532 1
1.0%

Interactions

2023-12-12T11:19:04.012974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:57.575117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:58.319877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:59.240985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:00.246883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:01.114152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:02.021944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:03.204293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:04.093867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:57.669503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:58.409953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:59.364651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:00.343443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:01.236644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:02.113363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:03.308647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:04.208428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:57.768863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:58.540878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:59.518152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:00.467204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:01.365379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:02.209832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:03.432861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:04.321531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:57.862125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:58.655308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:59.641946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:00.591462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:01.473123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:02.310948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:03.547626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:04.411665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:57.955059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:58.753259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:59.760551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:00.693647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:01.573951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:02.731748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:03.638002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:04.511512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:58.050041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:58.850423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:59.897439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:00.811341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:01.698451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:02.845177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:03.751389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:04.602388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:58.152107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:58.998263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:00.029728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:00.930172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:01.806670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:02.971400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:03.851022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:04.694211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:58.228982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:18:59.124731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:00.142776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:01.027715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:01.915152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:03.090564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:03.931030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:19:09.441290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단독범학교동창교도소_소년원동료직장동료친인척군동료동네친구고향친구애인기타미상
단독범1.0000.7050.7120.8790.8110.6590.7150.6510.6770.9260.688
학교동창0.7051.0000.6910.7700.8560.2570.5360.9510.8910.7790.910
교도소_소년원동료0.7120.6911.0000.8300.9400.0000.8860.8360.7110.8300.626
직장동료0.8790.7700.8301.0000.8660.8660.8660.8790.6560.9750.855
친인척0.8110.8560.9400.8661.0000.9870.9400.8200.9160.8710.840
군동료0.6590.2570.0000.8660.9871.0000.0000.0000.3690.6790.727
동네친구0.7150.5360.8860.8660.9400.0001.0000.8710.7110.9800.258
고향친구0.6510.9510.8360.8790.8200.0000.8711.0000.9310.9120.853
애인0.6770.8910.7110.6560.9160.3690.7110.9311.0000.7730.795
기타0.9260.7790.8300.9750.8710.6790.9800.9120.7731.0000.689
미상0.6880.9100.6260.8550.8400.7270.2580.8530.7950.6891.000
2023-12-12T11:19:09.613745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
군동료학교동창교도소_소년원동료
군동료1.0000.2110.000
학교동창0.2111.0000.622
교도소_소년원동료0.0000.6221.000
2023-12-12T11:19:10.033836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단독범직장동료친인척동네친구고향친구애인기타미상학교동창교도소_소년원동료군동료
단독범1.0000.5680.5650.5950.4220.4670.7580.7700.5680.5390.483
직장동료0.5681.0000.6580.5730.5430.4910.7440.6000.6450.6810.728
친인척0.5650.6581.0000.7300.4970.5360.6950.6650.7370.6670.827
동네친구0.5950.5730.7301.0000.6040.5850.6590.5530.4600.5600.000
고향친구0.4220.5430.4970.6041.0000.5850.5210.4670.6880.8040.000
애인0.4670.4910.5360.5850.5851.0000.4960.4270.5510.6450.306
기타0.7580.7440.6950.6590.5210.4961.0000.8210.6570.6810.504
미상0.7700.6000.6650.5530.4670.4270.8211.0000.5870.5520.664
학교동창0.5680.6450.7370.4600.6880.5510.6570.5871.0000.6220.211
교도소_소년원동료0.5390.6810.6670.5600.8040.6450.6810.5520.6221.0000.000
군동료0.4830.7280.8270.0000.0000.3060.5040.6640.2110.0001.000

Missing values

2023-12-12T11:19:04.843960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:19:05.077316image/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

범죄분류단독범학교동창교도소_소년원동료직장동료친인척군동료동네친구고향친구애인기타미상
0절도1044729551760158919289323
1장물1010002020125
2사기4702431561580291288404157
3횡령240400525441313151912
4배임360003412010185662
5손괴23790014100102271180
6살인7800000000010
7강도180001000016
8방화810001000001
9성폭력1731100000001242
범죄분류단독범학교동창교도소_소년원동료직장동료친인척군동료동네친구고향친구애인기타미상
95청소년보호법647007704002922
96축산물 위생관리법6200410210713
97출입국관리법8800000100816
98통신비밀 보호법30000000004
99특가법(도주차량)4980000000019
100폐기물관리법110001000001266
101풍속영업의 규제에 관한법률130000000021
102학원의 설립운영 및 과외교습에 관한법률50000000000
103화물자동차 운수사업법1540020020065
104기타특별법444320196103140329191376