Overview

Dataset statistics

Number of variables14
Number of observations190
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.3 KiB
Average record size in memory125.7 B

Variable types

Text1
Numeric12
Categorical1

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 인천지방검찰청의 범죄자 처분결과에 관한 통계임.
Author대검찰청
URLhttps://www.data.go.kr/data/15084960/fileData.do

Alerts

구공판_구속 is highly overall correlated with 구공판_불구속 and 2 other fieldsHigh correlation
구공판_불구속 is highly overall correlated with 구공판_구속 and 7 other fieldsHigh correlation
구약식 is highly overall correlated with 구공판_불구속 and 5 other fieldsHigh correlation
소년보호송치 is highly overall correlated with 구공판_불구속 and 4 other fieldsHigh correlation
가정보호송치 is highly overall correlated with 아동보호송치High correlation
아동보호송치 is highly overall correlated with 가정보호송치High correlation
기소유예 is highly overall correlated with 구공판_불구속 and 6 other fieldsHigh correlation
혐의없음 is highly overall correlated with 구공판_불구속 and 5 other fieldsHigh correlation
죄가안됨 is highly overall correlated with 소년보호송치High correlation
공소권없음 is highly overall correlated with 구공판_불구속 and 5 other fieldsHigh correlation
기소중지 is highly overall correlated with 구공판_구속 and 7 other fieldsHigh correlation
참고인중지 is highly overall correlated with 구공판_불구속 and 4 other fieldsHigh correlation
성매매보호송치 is highly overall correlated with 구공판_구속 and 2 other fieldsHigh correlation
성매매보호송치 is highly imbalanced (94.0%)Imbalance
범죄분류 has unique valuesUnique
구공판_구속 has 87 (45.8%) zerosZeros
구공판_불구속 has 41 (21.6%) zerosZeros
구약식 has 29 (15.3%) zerosZeros
소년보호송치 has 128 (67.4%) zerosZeros
가정보호송치 has 174 (91.6%) zerosZeros
아동보호송치 has 180 (94.7%) zerosZeros
기소유예 has 24 (12.6%) zerosZeros
혐의없음 has 15 (7.9%) zerosZeros
죄가안됨 has 162 (85.3%) zerosZeros
공소권없음 has 67 (35.3%) zerosZeros
기소중지 has 79 (41.6%) zerosZeros
참고인중지 has 131 (68.9%) zerosZeros

Reproduction

Analysis started2023-12-12 17:31:56.633999
Analysis finished2023-12-12 17:32:11.302132
Duration14.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct190
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-13T02:32:11.486600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length23.5
Mean length8.3263158
Min length2

Characters and Unicode

Total characters1582
Distinct characters242
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

Unique190 ?
Unique (%)100.0%

Sample

1st row절도
2nd row불법사용
3rd row침입절도
4th row장물
5th row사기
ValueCountFrequency (%)
관한법률 26
 
8.2%
22
 
7.0%
관리에 3
 
0.9%
마약류관리에 3
 
0.9%
보장법 2
 
0.6%
사업법 2
 
0.6%
아동·청소년의 2
 
0.6%
성보호에 2
 
0.6%
규제 2
 
0.6%
규제에 2
 
0.6%
Other values (248) 250
79.1%
2023-12-13T02:32:11.913731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
126
 
8.0%
114
 
7.2%
63
 
4.0%
37
 
2.3%
37
 
2.3%
32
 
2.0%
29
 
1.8%
28
 
1.8%
25
 
1.6%
24
 
1.5%
Other values (232) 1067
67.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1408
89.0%
Space Separator 126
 
8.0%
Other Punctuation 18
 
1.1%
Close Punctuation 15
 
0.9%
Open Punctuation 15
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
114
 
8.1%
63
 
4.5%
37
 
2.6%
37
 
2.6%
32
 
2.3%
29
 
2.1%
28
 
2.0%
25
 
1.8%
24
 
1.7%
23
 
1.6%
Other values (226) 996
70.7%
Other Punctuation
ValueCountFrequency (%)
, 12
66.7%
· 4
 
22.2%
/ 2
 
11.1%
Space Separator
ValueCountFrequency (%)
126
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1408
89.0%
Common 174
 
11.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
114
 
8.1%
63
 
4.5%
37
 
2.6%
37
 
2.6%
32
 
2.3%
29
 
2.1%
28
 
2.0%
25
 
1.8%
24
 
1.7%
23
 
1.6%
Other values (226) 996
70.7%
Common
ValueCountFrequency (%)
126
72.4%
) 15
 
8.6%
( 15
 
8.6%
, 12
 
6.9%
· 4
 
2.3%
/ 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1408
89.0%
ASCII 170
 
10.7%
None 4
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
126
74.1%
) 15
 
8.8%
( 15
 
8.8%
, 12
 
7.1%
/ 2
 
1.2%
Hangul
ValueCountFrequency (%)
114
 
8.1%
63
 
4.5%
37
 
2.6%
37
 
2.6%
32
 
2.3%
29
 
2.1%
28
 
2.0%
25
 
1.8%
24
 
1.7%
23
 
1.6%
Other values (226) 996
70.7%
None
ValueCountFrequency (%)
· 4
100.0%

구공판_구속
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.494737
Minimum0
Maximum535
Zeros87
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T02:32:12.073708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile51.4
Maximum535
Range535
Interquartile range (IQR)6

Descriptive statistics

Standard deviation52.533159
Coefficient of variation (CV)3.8928628
Kurtosis63.446462
Mean13.494737
Median Absolute Deviation (MAD)1
Skewness7.4871865
Sum2564
Variance2759.7328
MonotonicityNot monotonic
2023-12-13T02:32:12.263471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 87
45.8%
1 16
 
8.4%
3 12
 
6.3%
2 12
 
6.3%
5 7
 
3.7%
4 6
 
3.2%
6 6
 
3.2%
8 5
 
2.6%
7 3
 
1.6%
40 2
 
1.1%
Other values (28) 34
 
17.9%
ValueCountFrequency (%)
0 87
45.8%
1 16
 
8.4%
2 12
 
6.3%
3 12
 
6.3%
4 6
 
3.2%
5 7
 
3.7%
6 6
 
3.2%
7 3
 
1.6%
8 5
 
2.6%
9 2
 
1.1%
ValueCountFrequency (%)
535 1
0.5%
353 1
0.5%
282 1
0.5%
132 1
0.5%
84 1
0.5%
76 1
0.5%
72 1
0.5%
67 1
0.5%
65 1
0.5%
55 1
0.5%

구공판_불구속
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)37.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.110526
Minimum0
Maximum2521
Zeros41
Zeros (%)21.6%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T02:32:12.420491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6.5
Q334
95-th percentile302.15
Maximum2521
Range2521
Interquartile range (IQR)33

Descriptive statistics

Standard deviation228.84484
Coefficient of variation (CV)3.4099694
Kurtosis72.88989
Mean67.110526
Median Absolute Deviation (MAD)6.5
Skewness7.6105586
Sum12751
Variance52369.961
MonotonicityNot monotonic
2023-12-13T02:32:12.569691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41
21.6%
1 15
 
7.9%
3 13
 
6.8%
2 12
 
6.3%
6 6
 
3.2%
5 5
 
2.6%
16 4
 
2.1%
8 4
 
2.1%
34 3
 
1.6%
13 3
 
1.6%
Other values (61) 84
44.2%
ValueCountFrequency (%)
0 41
21.6%
1 15
 
7.9%
2 12
 
6.3%
3 13
 
6.8%
4 3
 
1.6%
5 5
 
2.6%
6 6
 
3.2%
7 2
 
1.1%
8 4
 
2.1%
9 2
 
1.1%
ValueCountFrequency (%)
2521 1
0.5%
1123 1
0.5%
688 1
0.5%
680 1
0.5%
659 1
0.5%
648 1
0.5%
556 1
0.5%
452 1
0.5%
395 1
0.5%
326 1
0.5%

구약식
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.00526
Minimum0
Maximum12443
Zeros29
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T02:32:12.741056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median26
Q3122.75
95-th percentile1347.65
Maximum12443
Range12443
Interquartile range (IQR)119.75

Descriptive statistics

Standard deviation1019.7861
Coefficient of variation (CV)3.8050974
Kurtosis108.8583
Mean268.00526
Median Absolute Deviation (MAD)26
Skewness9.5023274
Sum50921
Variance1039963.7
MonotonicityNot monotonic
2023-12-13T02:32:12.898636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
15.3%
1 8
 
4.2%
2 7
 
3.7%
4 6
 
3.2%
5 5
 
2.6%
3 5
 
2.6%
8 4
 
2.1%
31 4
 
2.1%
6 4
 
2.1%
14 4
 
2.1%
Other values (85) 114
60.0%
ValueCountFrequency (%)
0 29
15.3%
1 8
 
4.2%
2 7
 
3.7%
3 5
 
2.6%
4 6
 
3.2%
5 5
 
2.6%
6 4
 
2.1%
7 2
 
1.1%
8 4
 
2.1%
9 2
 
1.1%
ValueCountFrequency (%)
12443 1
0.5%
3039 1
0.5%
2839 1
0.5%
2641 1
0.5%
2325 1
0.5%
2223 1
0.5%
2053 1
0.5%
1627 1
0.5%
1538 1
0.5%
1529 1
0.5%

소년보호송치
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.021053
Minimum0
Maximum1159
Zeros128
Zeros (%)67.4%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T02:32:13.038003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile36.1
Maximum1159
Range1159
Interquartile range (IQR)2

Descriptive statistics

Standard deviation92.626174
Coefficient of variation (CV)5.7815286
Kurtosis125.09366
Mean16.021053
Median Absolute Deviation (MAD)0
Skewness10.534346
Sum3044
Variance8579.608
MonotonicityNot monotonic
2023-12-13T02:32:13.173615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 128
67.4%
1 13
 
6.8%
6 4
 
2.1%
4 4
 
2.1%
19 3
 
1.6%
10 3
 
1.6%
7 3
 
1.6%
14 2
 
1.1%
8 2
 
1.1%
17 2
 
1.1%
Other values (23) 26
 
13.7%
ValueCountFrequency (%)
0 128
67.4%
1 13
 
6.8%
2 2
 
1.1%
3 1
 
0.5%
4 4
 
2.1%
5 1
 
0.5%
6 4
 
2.1%
7 3
 
1.6%
8 2
 
1.1%
9 1
 
0.5%
ValueCountFrequency (%)
1159 1
0.5%
325 1
0.5%
290 1
0.5%
284 1
0.5%
149 1
0.5%
118 1
0.5%
71 1
0.5%
54 1
0.5%
42 1
0.5%
37 1
0.5%

가정보호송치
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.773684
Minimum0
Maximum1674
Zeros174
Zeros (%)91.6%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T02:32:13.284726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.55
Maximum1674
Range1674
Interquartile range (IQR)0

Descriptive statistics

Standard deviation123.06176
Coefficient of variation (CV)10.452273
Kurtosis178.7985
Mean11.773684
Median Absolute Deviation (MAD)0
Skewness13.211008
Sum2237
Variance15144.197
MonotonicityNot monotonic
2023-12-13T02:32:13.397259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 174
91.6%
1 3
 
1.6%
2 3
 
1.6%
3 2
 
1.1%
155 1
 
0.5%
5 1
 
0.5%
1674 1
 
0.5%
10 1
 
0.5%
196 1
 
0.5%
152 1
 
0.5%
Other values (2) 2
 
1.1%
ValueCountFrequency (%)
0 174
91.6%
1 3
 
1.6%
2 3
 
1.6%
3 2
 
1.1%
5 1
 
0.5%
6 1
 
0.5%
10 1
 
0.5%
24 1
 
0.5%
152 1
 
0.5%
155 1
 
0.5%
ValueCountFrequency (%)
1674 1
 
0.5%
196 1
 
0.5%
155 1
 
0.5%
152 1
 
0.5%
24 1
 
0.5%
10 1
 
0.5%
6 1
 
0.5%
5 1
 
0.5%
3 2
1.1%
2 3
1.6%

성매매보호송치
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
188 
1
 
1
49
 
1

Length

Max length2
Median length1
Mean length1.0052632
Min length1

Unique

Unique2 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 188
98.9%
1 1
 
0.5%
49 1
 
0.5%

Length

2023-12-13T02:32:13.815289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:32:13.927748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 188
98.9%
1 1
 
0.5%
49 1
 
0.5%

아동보호송치
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3105263
Minimum0
Maximum205
Zeros180
Zeros (%)94.7%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T02:32:14.032721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.55
Maximum205
Range205
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.010169
Coefficient of variation (CV)11.453543
Kurtosis182.22224
Mean1.3105263
Median Absolute Deviation (MAD)0
Skewness13.395367
Sum249
Variance225.30518
MonotonicityNot monotonic
2023-12-13T02:32:14.130411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 180
94.7%
1 5
 
2.6%
2 1
 
0.5%
29 1
 
0.5%
3 1
 
0.5%
5 1
 
0.5%
205 1
 
0.5%
ValueCountFrequency (%)
0 180
94.7%
1 5
 
2.6%
2 1
 
0.5%
3 1
 
0.5%
5 1
 
0.5%
29 1
 
0.5%
205 1
 
0.5%
ValueCountFrequency (%)
205 1
 
0.5%
29 1
 
0.5%
5 1
 
0.5%
3 1
 
0.5%
2 1
 
0.5%
1 5
 
2.6%
0 180
94.7%

기소유예
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct90
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.42105
Minimum0
Maximum4817
Zeros24
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T02:32:14.256894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median15.5
Q358.5
95-th percentile702.3
Maximum4817
Range4817
Interquartile range (IQR)56.5

Descriptive statistics

Standard deviation536.82928
Coefficient of variation (CV)3.4540319
Kurtosis46.862424
Mean155.42105
Median Absolute Deviation (MAD)14.5
Skewness6.3794222
Sum29530
Variance288185.67
MonotonicityNot monotonic
2023-12-13T02:32:14.392528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24
 
12.6%
1 14
 
7.4%
2 13
 
6.8%
3 7
 
3.7%
10 5
 
2.6%
5 5
 
2.6%
13 5
 
2.6%
8 4
 
2.1%
39 4
 
2.1%
6 4
 
2.1%
Other values (80) 105
55.3%
ValueCountFrequency (%)
0 24
12.6%
1 14
7.4%
2 13
6.8%
3 7
 
3.7%
4 3
 
1.6%
5 5
 
2.6%
6 4
 
2.1%
7 3
 
1.6%
8 4
 
2.1%
9 2
 
1.1%
ValueCountFrequency (%)
4817 1
0.5%
4141 1
0.5%
2292 1
0.5%
1712 1
0.5%
1543 1
0.5%
1320 1
0.5%
1049 1
0.5%
1047 1
0.5%
1022 1
0.5%
705 1
0.5%

혐의없음
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct94
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.16842
Minimum0
Maximum7420
Zeros15
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T02:32:14.508034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median18
Q376.75
95-th percentile661.65
Maximum7420
Range7420
Interquartile range (IQR)71.75

Descriptive statistics

Standard deviation599.52032
Coefficient of variation (CV)3.9659098
Kurtosis116.47195
Mean151.16842
Median Absolute Deviation (MAD)16.5
Skewness9.9832618
Sum28722
Variance359424.62
MonotonicityNot monotonic
2023-12-13T02:32:14.632380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
7.9%
2 15
 
7.9%
5 11
 
5.8%
13 7
 
3.7%
1 7
 
3.7%
6 6
 
3.2%
10 5
 
2.6%
7 4
 
2.1%
4 4
 
2.1%
20 4
 
2.1%
Other values (84) 112
58.9%
ValueCountFrequency (%)
0 15
7.9%
1 7
3.7%
2 15
7.9%
3 3
 
1.6%
4 4
 
2.1%
5 11
5.8%
6 6
 
3.2%
7 4
 
2.1%
8 3
 
1.6%
9 3
 
1.6%
ValueCountFrequency (%)
7420 1
0.5%
2492 1
0.5%
1323 1
0.5%
1138 1
0.5%
1080 1
0.5%
1048 1
0.5%
957 1
0.5%
906 1
0.5%
723 1
0.5%
672 1
0.5%

죄가안됨
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6421053
Minimum0
Maximum143
Zeros162
Zeros (%)85.3%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T02:32:14.734428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.55
Maximum143
Range143
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.327651
Coefficient of variation (CV)6.898249
Kurtosis131.29022
Mean1.6421053
Median Absolute Deviation (MAD)0
Skewness10.914232
Sum312
Variance128.31568
MonotonicityNot monotonic
2023-12-13T02:32:14.837937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 162
85.3%
1 13
 
6.8%
4 3
 
1.6%
2 3
 
1.6%
3 2
 
1.1%
10 1
 
0.5%
143 1
 
0.5%
41 1
 
0.5%
17 1
 
0.5%
44 1
 
0.5%
Other values (2) 2
 
1.1%
ValueCountFrequency (%)
0 162
85.3%
1 13
 
6.8%
2 3
 
1.6%
3 2
 
1.1%
4 3
 
1.6%
7 1
 
0.5%
10 1
 
0.5%
13 1
 
0.5%
17 1
 
0.5%
41 1
 
0.5%
ValueCountFrequency (%)
143 1
 
0.5%
44 1
 
0.5%
41 1
 
0.5%
17 1
 
0.5%
13 1
 
0.5%
10 1
 
0.5%
7 1
 
0.5%
4 3
1.6%
3 2
1.1%
2 3
1.6%

공소권없음
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.09474
Minimum0
Maximum12582
Zeros67
Zeros (%)35.3%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T02:32:14.995565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37
95-th percentile248.45
Maximum12582
Range12582
Interquartile range (IQR)7

Descriptive statistics

Standard deviation1037.5765
Coefficient of variation (CV)7.7957739
Kurtosis119.29543
Mean133.09474
Median Absolute Deviation (MAD)1
Skewness10.640075
Sum25288
Variance1076564.9
MonotonicityNot monotonic
2023-12-13T02:32:15.136793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 67
35.3%
1 33
17.4%
2 11
 
5.8%
3 10
 
5.3%
7 8
 
4.2%
5 6
 
3.2%
4 6
 
3.2%
8 4
 
2.1%
12 3
 
1.6%
13 3
 
1.6%
Other values (34) 39
20.5%
ValueCountFrequency (%)
0 67
35.3%
1 33
17.4%
2 11
 
5.8%
3 10
 
5.3%
4 6
 
3.2%
5 6
 
3.2%
6 3
 
1.6%
7 8
 
4.2%
8 4
 
2.1%
11 2
 
1.1%
ValueCountFrequency (%)
12582 1
0.5%
6773 1
0.5%
988 1
0.5%
708 1
0.5%
632 1
0.5%
497 1
0.5%
310 1
0.5%
305 1
0.5%
279 1
0.5%
257 1
0.5%

기소중지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.852632
Minimum0
Maximum2058
Zeros79
Zeros (%)41.6%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T02:32:15.265815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38.75
95-th percentile105.2
Maximum2058
Range2058
Interquartile range (IQR)8.75

Descriptive statistics

Standard deviation162.78934
Coefficient of variation (CV)5.2763519
Kurtosis129.7541
Mean30.852632
Median Absolute Deviation (MAD)1
Skewness10.768669
Sum5862
Variance26500.37
MonotonicityNot monotonic
2023-12-13T02:32:15.393197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 79
41.6%
1 22
 
11.6%
3 12
 
6.3%
2 11
 
5.8%
4 7
 
3.7%
7 4
 
2.1%
15 4
 
2.1%
5 3
 
1.6%
37 3
 
1.6%
16 2
 
1.1%
Other values (35) 43
22.6%
ValueCountFrequency (%)
0 79
41.6%
1 22
 
11.6%
2 11
 
5.8%
3 12
 
6.3%
4 7
 
3.7%
5 3
 
1.6%
6 2
 
1.1%
7 4
 
2.1%
8 2
 
1.1%
9 2
 
1.1%
ValueCountFrequency (%)
2058 1
0.5%
606 1
0.5%
551 1
0.5%
297 1
0.5%
209 1
0.5%
158 1
0.5%
136 1
0.5%
125 1
0.5%
111 1
0.5%
107 1
0.5%

참고인중지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2894737
Minimum0
Maximum278
Zeros131
Zeros (%)68.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T02:32:15.489989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile10.65
Maximum278
Range278
Interquartile range (IQR)1

Descriptive statistics

Standard deviation20.727391
Coefficient of variation (CV)6.301127
Kurtosis165.55778
Mean3.2894737
Median Absolute Deviation (MAD)0
Skewness12.509655
Sum625
Variance429.62476
MonotonicityNot monotonic
2023-12-13T02:32:15.604914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 131
68.9%
1 15
 
7.9%
2 13
 
6.8%
3 8
 
4.2%
5 5
 
2.6%
6 3
 
1.6%
7 2
 
1.1%
4 2
 
1.1%
30 1
 
0.5%
37 1
 
0.5%
Other values (9) 9
 
4.7%
ValueCountFrequency (%)
0 131
68.9%
1 15
 
7.9%
2 13
 
6.8%
3 8
 
4.2%
4 2
 
1.1%
5 5
 
2.6%
6 3
 
1.6%
7 2
 
1.1%
9 1
 
0.5%
12 1
 
0.5%
ValueCountFrequency (%)
278 1
0.5%
37 1
0.5%
32 1
0.5%
30 1
0.5%
24 1
0.5%
23 1
0.5%
19 1
0.5%
17 1
0.5%
14 1
0.5%
12 1
0.5%

Interactions

2023-12-13T02:32:09.622020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:57.106883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:58.105801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:59.140378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:00.182692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:01.580498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:02.707128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:03.869687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:05.081831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:06.196608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:07.105338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:08.461362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:09.760343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:57.174936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:58.198017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:59.213550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:00.262768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:01.682840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:02.794251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:03.950633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:05.172879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:06.274832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:07.436014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:08.535866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:09.850341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:57.266706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:58.288322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:59.286488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:00.336089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:01.785756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:02.884605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:04.042210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:05.253060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:06.349703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:07.513776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:08.612434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:09.943272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:57.356286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:58.363573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:59.364100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:00.424996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:01.887558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:02.988954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:04.122002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:05.342218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:06.423200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:07.599659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:08.698739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:10.069731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:57.457765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:58.462215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:59.450766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:00.518751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:01.989302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:03.110472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:04.255963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:05.449245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:06.498891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:07.678411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:08.788789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:10.200520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:57.537161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:58.543958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:59.548324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:00.615468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:02.082939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:03.229182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:04.357202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:05.559730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:06.569997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:07.765705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:08.893993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:10.292174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:57.606354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:58.618306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:59.639165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:00.691339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:02.166164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:03.313306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:04.440921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:05.640455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:06.637449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:07.863934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:09.015529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:10.409884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:57.689023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:58.696238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:59.734771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:00.790956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:02.247169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:03.392320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:04.525795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:05.716895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:06.707966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:07.954129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:09.097529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:10.516636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:57.772897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:58.780073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:59.836914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:00.884765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:02.337729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:03.479436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:04.666720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:05.800446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:06.781938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:08.065227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:09.201943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:10.629339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:57.860144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:58.880414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:59.931003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:00.995394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:02.420680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:03.594830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:04.769656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:05.922940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:06.858167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:08.182825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:09.320000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:10.726324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:57.943161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:58.983565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:00.021561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:01.076316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:02.514578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:03.699230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:04.873650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:06.030128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:06.940408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:08.266943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:09.431629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:10.830635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:58.019509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:59.067433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:00.106849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:01.158948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:02.606016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:03.790230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:04.994827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:06.110163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:07.026394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:08.362797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:32:09.531208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:32:15.764078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구공판_구속구공판_불구속구약식소년보호송치가정보호송치성매매보호송치아동보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지
구공판_구속1.0000.7770.4820.8150.4990.9370.0000.8310.7730.1790.0000.7280.945
구공판_불구속0.7771.0000.7760.5930.4900.3480.3480.7620.7280.6320.4550.6530.739
구약식0.4820.7761.0000.7820.4860.2690.3980.5580.7460.4510.5590.7510.356
소년보호송치0.8150.5930.7821.0000.5590.6720.3980.8110.8020.5240.3980.6570.398
가정보호송치0.4990.4900.4860.5591.0000.0000.9410.6830.2691.0000.9410.0000.000
성매매보호송치0.9370.3480.2690.6720.0001.0000.0000.8200.2690.0000.0000.0000.000
아동보호송치0.0000.3480.3980.3980.9410.0001.0000.5820.2690.7120.9410.0000.000
기소유예0.8310.7620.5580.8110.6830.8200.5821.0000.9230.6310.5820.7490.837
혐의없음0.7730.7280.7460.8020.2690.2690.2690.9231.0000.2240.2690.9340.730
죄가안됨0.1790.6320.4510.5241.0000.0000.7120.6310.2241.0000.7120.0000.000
공소권없음0.0000.4550.5590.3980.9410.0000.9410.5820.2690.7121.0000.0000.000
기소중지0.7280.6530.7510.6570.0000.0000.0000.7490.9340.0000.0001.0000.717
참고인중지0.9450.7390.3560.3980.0000.0000.0000.8370.7300.0000.0000.7171.000
2023-12-13T02:32:15.902423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구공판_구속구공판_불구속구약식소년보호송치가정보호송치아동보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지성매매보호송치
구공판_구속1.0000.6780.2230.5000.2380.2610.3900.3640.3240.4120.5660.4660.692
구공판_불구속0.6781.0000.5970.5430.2530.2740.6630.6820.4190.6410.7970.5860.279
구약식0.2230.5971.0000.4110.1750.1710.7980.7870.3750.6420.6520.5090.257
소년보호송치0.5000.5430.4111.0000.3250.2790.5820.4950.5030.4350.5300.3990.700
가정보호송치0.2380.2530.1750.3251.0000.6280.2510.2540.4250.2840.1910.1780.000
아동보호송치0.2610.2740.1710.2790.6281.0000.2450.2890.3180.2320.2220.2140.000
기소유예0.3900.6630.7980.5820.2510.2451.0000.7560.4470.6270.7440.4840.773
혐의없음0.3640.6820.7870.4950.2540.2890.7561.0000.4860.7070.7160.5820.257
죄가안됨0.3240.4190.3750.5030.4250.3180.4470.4861.0000.3790.4000.3120.000
공소권없음0.4120.6410.6420.4350.2840.2320.6270.7070.3791.0000.7330.5810.000
기소중지0.5660.7970.6520.5300.1910.2220.7440.7160.4000.7331.0000.6430.000
참고인중지0.4660.5860.5090.3990.1780.2140.4840.5820.3120.5810.6431.0000.000
성매매보호송치0.6920.2790.2570.7000.0000.0000.7730.2570.0000.0000.0000.0001.000

Missing values

2023-12-13T02:32:10.991103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:32:11.201323image/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

범죄분류구공판_구속구공판_불구속구약식소년보호송치가정보호송치성매매보호송치아동보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지
0절도282648205311590104141113810851365
1불법사용226170001920110
2침입절도5593719000551701171
3장물4517421000136450033
4사기535252123252840004817742003052058278
5컴퓨터등사용사기3233500002424503562
6부당이득0000000020000
7편의시설부정이용0029200017320053
8전기통신금융사기피해금환급에관한특별법6131610002445304370
9보험사기방지특별법311580000383600150
범죄분류구공판_구속구공판_불구속구약식소년보호송치가정보호송치성매매보호송치아동보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지
180통신비밀보호법0230000050100
181특가법(도주차량)51409990003921401290
182특허법01400000170310
183폐기물관리법116110000017420130
184풍속영업의 규제에 관한법률05260000550000
185학교보건법0050000200000
186학원의 설립운영 및 과외교습에 관한법률001300001540000
187화물자동차운수사업법00246000065540800
188화학물질관리법37137000360000
189기타특별법65680283919101104710801747330