Overview

Dataset statistics

Number of variables11
Number of observations93
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.0 KiB
Average record size in memory99.4 B

Variable types

Text1
Numeric10

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 공무원범죄자의 생활정도 및 혼인관계에 따른 범죄 통계임.
Author대검찰청
URLhttps://www.data.go.kr/data/15084739/fileData.do

Alerts

생활정도_하류 is highly overall correlated with 생활정도_중류 and 7 other fieldsHigh correlation
생활정도_중류 is highly overall correlated with 생활정도_하류 and 8 other fieldsHigh correlation
생활정도_상류 is highly overall correlated with 생활정도_하류 and 7 other fieldsHigh correlation
생활정도_미상 is highly overall correlated with 생활정도_하류 and 6 other fieldsHigh correlation
유배우자 is highly overall correlated with 생활정도_하류 and 8 other fieldsHigh correlation
동거 is highly overall correlated with 생활정도_하류 and 5 other fieldsHigh correlation
이혼 is highly overall correlated with 생활정도_하류 and 7 other fieldsHigh correlation
사별 is highly overall correlated with 생활정도_중류 and 1 other fieldsHigh correlation
미혼 is highly overall correlated with 생활정도_하류 and 7 other fieldsHigh correlation
혼인관계_미상 is highly overall correlated with 생활정도_하류 and 6 other fieldsHigh correlation
범죄분류 has unique valuesUnique
생활정도_하류 has 34 (36.6%) zerosZeros
생활정도_중류 has 11 (11.8%) zerosZeros
생활정도_상류 has 57 (61.3%) zerosZeros
생활정도_미상 has 13 (14.0%) zerosZeros
유배우자 has 14 (15.1%) zerosZeros
동거 has 73 (78.5%) zerosZeros
이혼 has 54 (58.1%) zerosZeros
사별 has 75 (80.6%) zerosZeros
미혼 has 35 (37.6%) zerosZeros
혼인관계_미상 has 13 (14.0%) zerosZeros

Reproduction

Analysis started2023-12-12 10:31:01.552655
Analysis finished2023-12-12 10:31:13.277832
Duration11.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct93
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size876.0 B
2023-12-12T19:31:13.497530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length6.1827957
Min length2

Characters and Unicode

Total characters575
Distinct characters168
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

Unique93 ?
Unique (%)100.0%

Sample

1st row절도
2nd row장물
3rd row사기
4th row횡령
5th row배임
ValueCountFrequency (%)
절도 1
 
1.1%
경범죄처벌법 1
 
1.1%
선박안전법 1
 
1.1%
산지관리법 1
 
1.1%
산업안전보건법 1
 
1.1%
산림자원의조성및관리에관한법률 1
 
1.1%
부정수표단속법 1
 
1.1%
병역법 1
 
1.1%
물환경보전법 1
 
1.1%
마약류관리에관한법률 1
 
1.1%
Other values (83) 83
89.2%
2023-12-12T19:31:14.016056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49
 
8.5%
19
 
3.3%
15
 
2.6%
11
 
1.9%
11
 
1.9%
10
 
1.7%
9
 
1.6%
9
 
1.6%
9
 
1.6%
9
 
1.6%
Other values (158) 424
73.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 563
97.9%
Open Punctuation 4
 
0.7%
Close Punctuation 4
 
0.7%
Other Punctuation 4
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
49
 
8.7%
19
 
3.4%
15
 
2.7%
11
 
2.0%
11
 
2.0%
10
 
1.8%
9
 
1.6%
9
 
1.6%
9
 
1.6%
9
 
1.6%
Other values (154) 412
73.2%
Other Punctuation
ValueCountFrequency (%)
, 2
50.0%
· 2
50.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 563
97.9%
Common 12
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
49
 
8.7%
19
 
3.4%
15
 
2.7%
11
 
2.0%
11
 
2.0%
10
 
1.8%
9
 
1.6%
9
 
1.6%
9
 
1.6%
9
 
1.6%
Other values (154) 412
73.2%
Common
ValueCountFrequency (%)
( 4
33.3%
) 4
33.3%
, 2
16.7%
· 2
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 563
97.9%
ASCII 10
 
1.7%
None 2
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
49
 
8.7%
19
 
3.4%
15
 
2.7%
11
 
2.0%
11
 
2.0%
10
 
1.8%
9
 
1.6%
9
 
1.6%
9
 
1.6%
9
 
1.6%
Other values (154) 412
73.2%
ASCII
ValueCountFrequency (%)
( 4
40.0%
) 4
40.0%
, 2
20.0%
None
ValueCountFrequency (%)
· 2
100.0%

생활정도_하류
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.322581
Minimum0
Maximum639
Zeros34
Zeros (%)36.6%
Negative0
Negative (%)0.0%
Memory size969.0 B
2023-12-12T19:31:14.197234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q312
95-th percentile59.6
Maximum639
Range639
Interquartile range (IQR)12

Descriptive statistics

Standard deviation73.347914
Coefficient of variation (CV)3.7959689
Kurtosis58.069475
Mean19.322581
Median Absolute Deviation (MAD)1
Skewness7.2514551
Sum1797
Variance5379.9165
MonotonicityNot monotonic
2023-12-12T19:31:14.370339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 34
36.6%
1 16
17.2%
5 4
 
4.3%
2 4
 
4.3%
4 4
 
4.3%
6 3
 
3.2%
17 3
 
3.2%
3 2
 
2.2%
27 2
 
2.2%
105 1
 
1.1%
Other values (20) 20
21.5%
ValueCountFrequency (%)
0 34
36.6%
1 16
17.2%
2 4
 
4.3%
3 2
 
2.2%
4 4
 
4.3%
5 4
 
4.3%
6 3
 
3.2%
7 1
 
1.1%
10 1
 
1.1%
12 1
 
1.1%
ValueCountFrequency (%)
639 1
1.1%
286 1
1.1%
105 1
1.1%
81 1
1.1%
68 1
1.1%
54 1
1.1%
49 1
1.1%
43 1
1.1%
41 1
1.1%
40 1
1.1%

생활정도_중류
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.215054
Minimum0
Maximum1128
Zeros11
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size969.0 B
2023-12-12T19:31:14.520944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q331
95-th percentile216.6
Maximum1128
Range1128
Interquartile range (IQR)30

Descriptive statistics

Standard deviation145.00561
Coefficient of variation (CV)2.887692
Kurtosis36.774045
Mean50.215054
Median Absolute Deviation (MAD)5
Skewness5.6257534
Sum4670
Variance21026.627
MonotonicityNot monotonic
2023-12-12T19:31:14.707273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1 16
17.2%
0 11
 
11.8%
2 6
 
6.5%
9 4
 
4.3%
12 4
 
4.3%
3 4
 
4.3%
4 4
 
4.3%
6 4
 
4.3%
5 3
 
3.2%
8 3
 
3.2%
Other values (33) 34
36.6%
ValueCountFrequency (%)
0 11
11.8%
1 16
17.2%
2 6
 
6.5%
3 4
 
4.3%
4 4
 
4.3%
5 3
 
3.2%
6 4
 
4.3%
7 1
 
1.1%
8 3
 
3.2%
9 4
 
4.3%
ValueCountFrequency (%)
1128 1
1.1%
682 1
1.1%
328 1
1.1%
258 1
1.1%
255 1
1.1%
191 1
1.1%
179 1
1.1%
157 1
1.1%
146 1
1.1%
108 1
1.1%

생활정도_상류
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9139785
Minimum0
Maximum25
Zeros57
Zeros (%)61.3%
Negative0
Negative (%)0.0%
Memory size969.0 B
2023-12-12T19:31:14.838506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile10
Maximum25
Range25
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.0315493
Coefficient of variation (CV)2.1063712
Kurtosis13.256044
Mean1.9139785
Median Absolute Deviation (MAD)0
Skewness3.3057593
Sum178
Variance16.253389
MonotonicityNot monotonic
2023-12-12T19:31:14.965986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 57
61.3%
1 9
 
9.7%
2 7
 
7.5%
4 5
 
5.4%
5 3
 
3.2%
3 3
 
3.2%
10 3
 
3.2%
9 2
 
2.2%
16 1
 
1.1%
7 1
 
1.1%
Other values (2) 2
 
2.2%
ValueCountFrequency (%)
0 57
61.3%
1 9
 
9.7%
2 7
 
7.5%
3 3
 
3.2%
4 5
 
5.4%
5 3
 
3.2%
7 1
 
1.1%
9 2
 
2.2%
10 3
 
3.2%
15 1
 
1.1%
ValueCountFrequency (%)
25 1
 
1.1%
16 1
 
1.1%
15 1
 
1.1%
10 3
3.2%
9 2
 
2.2%
7 1
 
1.1%
5 3
3.2%
4 5
5.4%
3 3
3.2%
2 7
7.5%

생활정도_미상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.666667
Minimum0
Maximum1536
Zeros13
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size969.0 B
2023-12-12T19:31:15.117957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q348
95-th percentile336.2
Maximum1536
Range1536
Interquartile range (IQR)46

Descriptive statistics

Standard deviation199.75291
Coefficient of variation (CV)2.7872499
Kurtosis32.742796
Mean71.666667
Median Absolute Deviation (MAD)6
Skewness5.1872097
Sum6665
Variance39901.225
MonotonicityNot monotonic
2023-12-12T19:31:15.590212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 13
 
14.0%
3 10
 
10.8%
2 7
 
7.5%
1 7
 
7.5%
4 5
 
5.4%
8 3
 
3.2%
5 3
 
3.2%
33 3
 
3.2%
6 3
 
3.2%
63 2
 
2.2%
Other values (34) 37
39.8%
ValueCountFrequency (%)
0 13
14.0%
1 7
7.5%
2 7
7.5%
3 10
10.8%
4 5
 
5.4%
5 3
 
3.2%
6 3
 
3.2%
7 1
 
1.1%
8 3
 
3.2%
9 2
 
2.2%
ValueCountFrequency (%)
1536 1
1.1%
664 1
1.1%
660 1
1.1%
562 1
1.1%
389 1
1.1%
301 1
1.1%
290 1
1.1%
281 1
1.1%
267 1
1.1%
168 1
1.1%

유배우자
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.16129
Minimum0
Maximum1288
Zeros14
Zeros (%)15.1%
Negative0
Negative (%)0.0%
Memory size969.0 B
2023-12-12T19:31:15.734436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q332
95-th percentile215.2
Maximum1288
Range1288
Interquartile range (IQR)31

Descriptive statistics

Standard deviation161.26607
Coefficient of variation (CV)2.9775153
Kurtosis40.361739
Mean54.16129
Median Absolute Deviation (MAD)6
Skewness5.8955732
Sum5037
Variance26006.745
MonotonicityNot monotonic
2023-12-12T19:31:15.872120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 14
 
15.1%
0 14
 
15.1%
7 5
 
5.4%
3 5
 
5.4%
2 5
 
5.4%
6 4
 
4.3%
4 3
 
3.2%
10 3
 
3.2%
17 2
 
2.2%
5 2
 
2.2%
Other values (32) 36
38.7%
ValueCountFrequency (%)
0 14
15.1%
1 14
15.1%
2 5
 
5.4%
3 5
 
5.4%
4 3
 
3.2%
5 2
 
2.2%
6 4
 
4.3%
7 5
 
5.4%
9 2
 
2.2%
10 3
 
3.2%
ValueCountFrequency (%)
1288 1
1.1%
725 1
1.1%
360 1
1.1%
266 1
1.1%
226 1
1.1%
208 1
1.1%
200 1
1.1%
184 1
1.1%
167 1
1.1%
112 1
1.1%

동거
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.172043
Minimum0
Maximum40
Zeros73
Zeros (%)78.5%
Negative0
Negative (%)0.0%
Memory size969.0 B
2023-12-12T19:31:16.008571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.7836307
Coefficient of variation (CV)4.0814464
Kurtosis49.854634
Mean1.172043
Median Absolute Deviation (MAD)0
Skewness6.6669313
Sum109
Variance22.883123
MonotonicityNot monotonic
2023-12-12T19:31:16.138870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 73
78.5%
2 8
 
8.6%
1 5
 
5.4%
3 3
 
3.2%
8 1
 
1.1%
12 1
 
1.1%
19 1
 
1.1%
40 1
 
1.1%
ValueCountFrequency (%)
0 73
78.5%
1 5
 
5.4%
2 8
 
8.6%
3 3
 
3.2%
8 1
 
1.1%
12 1
 
1.1%
19 1
 
1.1%
40 1
 
1.1%
ValueCountFrequency (%)
40 1
 
1.1%
19 1
 
1.1%
12 1
 
1.1%
8 1
 
1.1%
3 3
 
3.2%
2 8
 
8.6%
1 5
 
5.4%
0 73
78.5%

이혼
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5268817
Minimum0
Maximum61
Zeros54
Zeros (%)58.1%
Negative0
Negative (%)0.0%
Memory size969.0 B
2023-12-12T19:31:16.255804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile11
Maximum61
Range61
Interquartile range (IQR)2

Descriptive statistics

Standard deviation7.180274
Coefficient of variation (CV)2.8415552
Kurtosis48.620264
Mean2.5268817
Median Absolute Deviation (MAD)0
Skewness6.3135338
Sum235
Variance51.556335
MonotonicityNot monotonic
2023-12-12T19:31:16.361443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 54
58.1%
1 13
 
14.0%
3 5
 
5.4%
2 5
 
5.4%
4 3
 
3.2%
10 2
 
2.2%
7 2
 
2.2%
11 2
 
2.2%
8 1
 
1.1%
16 1
 
1.1%
Other values (5) 5
 
5.4%
ValueCountFrequency (%)
0 54
58.1%
1 13
 
14.0%
2 5
 
5.4%
3 5
 
5.4%
4 3
 
3.2%
5 1
 
1.1%
6 1
 
1.1%
7 2
 
2.2%
8 1
 
1.1%
10 2
 
2.2%
ValueCountFrequency (%)
61 1
1.1%
20 1
1.1%
16 1
1.1%
13 1
1.1%
11 2
2.2%
10 2
2.2%
8 1
1.1%
7 2
2.2%
6 1
1.1%
5 1
1.1%

사별
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3655914
Minimum0
Maximum9
Zeros75
Zeros (%)80.6%
Negative0
Negative (%)0.0%
Memory size969.0 B
2023-12-12T19:31:16.475842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1304573
Coefficient of variation (CV)3.0921331
Kurtosis38.143643
Mean0.3655914
Median Absolute Deviation (MAD)0
Skewness5.5556384
Sum34
Variance1.2779336
MonotonicityNot monotonic
2023-12-12T19:31:16.605510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 75
80.6%
1 12
 
12.9%
2 3
 
3.2%
4 1
 
1.1%
3 1
 
1.1%
9 1
 
1.1%
ValueCountFrequency (%)
0 75
80.6%
1 12
 
12.9%
2 3
 
3.2%
3 1
 
1.1%
4 1
 
1.1%
9 1
 
1.1%
ValueCountFrequency (%)
9 1
 
1.1%
4 1
 
1.1%
3 1
 
1.1%
2 3
 
3.2%
1 12
 
12.9%
0 75
80.6%

미혼
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.247312
Minimum0
Maximum413
Zeros35
Zeros (%)37.6%
Negative0
Negative (%)0.0%
Memory size969.0 B
2023-12-12T19:31:16.709172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile36
Maximum413
Range413
Interquartile range (IQR)8

Descriptive statistics

Standard deviation49.640152
Coefficient of variation (CV)3.7471868
Kurtosis50.271698
Mean13.247312
Median Absolute Deviation (MAD)1
Skewness6.7992822
Sum1232
Variance2464.1447
MonotonicityNot monotonic
2023-12-12T19:31:16.846594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 35
37.6%
1 16
17.2%
2 5
 
5.4%
4 5
 
5.4%
5 4
 
4.3%
15 3
 
3.2%
3 3
 
3.2%
8 3
 
3.2%
36 2
 
2.2%
12 2
 
2.2%
Other values (13) 15
16.1%
ValueCountFrequency (%)
0 35
37.6%
1 16
17.2%
2 5
 
5.4%
3 3
 
3.2%
4 5
 
5.4%
5 4
 
4.3%
7 1
 
1.1%
8 3
 
3.2%
9 1
 
1.1%
11 1
 
1.1%
ValueCountFrequency (%)
413 1
1.1%
234 1
1.1%
76 1
1.1%
54 1
1.1%
36 2
2.2%
34 2
2.2%
25 1
1.1%
21 2
2.2%
20 1
1.1%
17 1
1.1%

혼인관계_미상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.645161
Minimum0
Maximum1539
Zeros13
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size969.0 B
2023-12-12T19:31:16.992164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q348
95-th percentile336.2
Maximum1539
Range1539
Interquartile range (IQR)46

Descriptive statistics

Standard deviation200.11104
Coefficient of variation (CV)2.7930852
Kurtosis32.784495
Mean71.645161
Median Absolute Deviation (MAD)6
Skewness5.1913529
Sum6663
Variance40044.427
MonotonicityNot monotonic
2023-12-12T19:31:17.198570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 13
 
14.0%
2 9
 
9.7%
3 9
 
9.7%
1 6
 
6.5%
4 5
 
5.4%
6 4
 
4.3%
8 3
 
3.2%
5 3
 
3.2%
33 3
 
3.2%
63 2
 
2.2%
Other values (30) 36
38.7%
ValueCountFrequency (%)
0 13
14.0%
1 6
6.5%
2 9
9.7%
3 9
9.7%
4 5
 
5.4%
5 3
 
3.2%
6 4
 
4.3%
8 3
 
3.2%
9 2
 
2.2%
12 1
 
1.1%
ValueCountFrequency (%)
1539 1
1.1%
664 2
2.2%
562 1
1.1%
386 1
1.1%
303 1
1.1%
289 1
1.1%
283 1
1.1%
266 1
1.1%
166 1
1.1%
108 1
1.1%

Interactions

2023-12-12T19:31:11.676589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:02.370316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:03.324954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:04.309885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:05.229911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:06.301328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:07.283605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:08.649629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:09.567788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:10.558410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:11.829571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:02.452516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:03.427604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:04.391546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:05.349051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:06.407545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:07.376358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:08.740093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:09.658319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:10.669027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:11.945355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:02.534413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:03.523345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:04.471502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:05.458090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:06.498553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:07.474611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:08.819903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:09.758608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:10.770911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:12.076472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:02.614552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:03.630891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:04.543973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:05.548504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:06.607507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:07.587156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:08.903750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:09.849505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:10.861133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:12.221192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:02.718389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:03.760222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:04.636580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:05.648585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:06.715397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:07.694347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:08.996704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:09.959356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:10.970400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:12.358168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:02.803229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:03.868589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:04.725887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:05.745233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:06.809787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:07.792591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:09.087448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:10.052636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:11.067932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:12.487396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:02.912398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:03.961461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:04.827508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:05.846317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:06.927858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:07.918253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:09.182667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:10.162452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:11.178747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:12.602176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:03.021841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:04.049414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:04.908124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:05.976876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:07.034093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:08.015384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:09.266513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:10.284631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:11.310666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:12.713811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:03.148607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:04.123336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:05.012367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:06.078457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:07.110070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:08.109396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:09.350450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:10.359648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:11.449903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:12.822860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:03.227624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:04.216001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:05.144844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:06.175844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:07.184836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:08.206876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:09.451999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:10.452299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:31:11.554285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:31:17.342584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
범죄분류생활정도_하류생활정도_중류생활정도_상류생활정도_미상유배우자동거이혼사별미혼혼인관계_미상
범죄분류1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
생활정도_하류1.0001.0000.9060.8370.8810.9330.9250.7480.9170.9870.881
생활정도_중류1.0000.9061.0000.8150.8020.9980.9820.9260.8300.9250.802
생활정도_상류1.0000.8370.8151.0000.7360.8190.8390.8370.7790.8670.736
생활정도_미상1.0000.8810.8020.7361.0000.8170.8480.7020.8730.8641.000
유배우자1.0000.9330.9980.8190.8171.0000.9910.9340.8380.8830.817
동거1.0000.9250.9820.8390.8480.9911.0000.9600.8000.9040.848
이혼1.0000.7480.9260.8370.7020.9340.9601.0000.6720.7930.702
사별1.0000.9170.8300.7790.8730.8380.8000.6721.0000.9400.873
미혼1.0000.9870.9250.8670.8640.8830.9040.7930.9401.0000.864
혼인관계_미상1.0000.8810.8020.7361.0000.8170.8480.7020.8730.8641.000
2023-12-12T19:31:17.533861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생활정도_하류생활정도_중류생활정도_상류생활정도_미상유배우자동거이혼사별미혼혼인관계_미상
생활정도_하류1.0000.8080.7560.6350.8320.5730.7990.4260.8410.636
생활정도_중류0.8081.0000.7880.7650.9620.5520.7950.5160.8200.767
생활정도_상류0.7560.7881.0000.7280.8140.6150.7240.4730.6840.732
생활정도_미상0.6350.7650.7281.0000.8040.4780.6370.4910.6130.999
유배우자0.8320.9620.8140.8041.0000.5240.7800.5230.7510.803
동거0.5730.5520.6150.4780.5241.0000.6010.3240.5270.478
이혼0.7990.7950.7240.6370.7800.6011.0000.3620.8040.637
사별0.4260.5160.4730.4910.5230.3240.3621.0000.3720.490
미혼0.8410.8200.6840.6130.7510.5270.8040.3721.0000.615
혼인관계_미상0.6360.7670.7320.9990.8030.4780.6370.4900.6151.000

Missing values

2023-12-12T19:31:13.009358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:31:13.197337image/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

범죄분류생활정도_하류생활정도_중류생활정도_상류생활정도_미상유배우자동거이혼사별미혼혼인관계_미상
0절도27108563902843663
1장물2200100030
2사기681574290184210034289
3횡령281054931123311794
4배임43925639010457
5손괴1785464851401664
6살인1301300011
7강도0401300011
8방화0100000010
9성폭력49258169222611627694
범죄분류생활정도_하류생활정도_중류생활정도_상류생활정도_미상유배우자동거이혼사별미혼혼인관계_미상
83저작권법130173000117
84전자금융거래법715191701239
85정보통신망이용촉진및정보보호등에관한법률1726363320201263
86주민등록법1602401022
87청소년보호법2000200000
88통신비밀보호법0003000003
89특가법(도주차량)152321730011817
90폐기물관리법0504500004
91화물자동차운수사업법0102100002
92기타특별법8125510267266407034266