Overview

Dataset statistics

Number of variables14
Number of observations193
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.7 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 부산지방검찰청의 범죄자 처분결과에 관한 통계임.
Author대검찰청
URLhttps://www.data.go.kr/data/15084909/fileData.do

Alerts

구공판_구속 is highly overall correlated with 구공판_불구속 and 1 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 4 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 6 other fieldsHigh correlation
죄가안됨 is highly overall correlated with 소년보호송치High correlation
공소권없음 is highly overall correlated with 구공판_불구속 and 4 other fieldsHigh correlation
기소중지 is highly overall correlated with 구공판_불구속 and 5 other fieldsHigh correlation
참고인중지 is highly overall correlated with 구공판_불구속 and 2 other fieldsHigh correlation
성매매보호송치 is highly overall correlated with 구공판_구속 and 2 other fieldsHigh correlation
성매매보호송치 is highly imbalanced (94.1%)Imbalance
범죄분류 has unique valuesUnique
구공판_구속 has 86 (44.6%) zerosZeros
구공판_불구속 has 46 (23.8%) zerosZeros
구약식 has 28 (14.5%) zerosZeros
소년보호송치 has 141 (73.1%) zerosZeros
가정보호송치 has 184 (95.3%) zerosZeros
아동보호송치 has 187 (96.9%) zerosZeros
기소유예 has 37 (19.2%) zerosZeros
혐의없음 has 21 (10.9%) zerosZeros
죄가안됨 has 165 (85.5%) zerosZeros
공소권없음 has 73 (37.8%) zerosZeros
기소중지 has 94 (48.7%) zerosZeros
참고인중지 has 143 (74.1%) zerosZeros

Reproduction

Analysis started2023-12-12 21:05:00.590975
Analysis finished2023-12-12 21:05:14.998697
Duration14.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct193
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-13T06:05:15.144661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length21
Mean length7.6165803
Min length2

Characters and Unicode

Total characters1470
Distinct characters242
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

Unique193 ?
Unique (%)100.0%

Sample

1st row절도
2nd row불법사용
3rd row침입절도
4th row장물
5th row사기
ValueCountFrequency (%)
절도 1
 
0.5%
공중위생관리법 1
 
0.5%
부동산실권리자명의등기에관한법률 1
 
0.5%
마약류관리에관한법률(대마 1
 
0.5%
마약류관리에관한법률(마약 1
 
0.5%
마약류관리에관한법률(향정 1
 
0.5%
물환경보전법 1
 
0.5%
배타적경제수역에서의외국인어업등에관한주권적권리의행사에관한법률 1
 
0.5%
범죄수익은닉의규제및처벌등에관한법률 1
 
0.5%
변호사법 1
 
0.5%
Other values (183) 183
94.8%
2023-12-13T06:05:15.445673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
115
 
7.8%
64
 
4.4%
38
 
2.6%
38
 
2.6%
32
 
2.2%
29
 
2.0%
28
 
1.9%
25
 
1.7%
24
 
1.6%
24
 
1.6%
Other values (232) 1053
71.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1422
96.7%
Other Punctuation 18
 
1.2%
Close Punctuation 15
 
1.0%
Open Punctuation 15
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
115
 
8.1%
64
 
4.5%
38
 
2.7%
38
 
2.7%
32
 
2.3%
29
 
2.0%
28
 
2.0%
25
 
1.8%
24
 
1.7%
24
 
1.7%
Other values (227) 1005
70.7%
Other Punctuation
ValueCountFrequency (%)
, 12
66.7%
· 4
 
22.2%
/ 2
 
11.1%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1422
96.7%
Common 48
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
115
 
8.1%
64
 
4.5%
38
 
2.7%
38
 
2.7%
32
 
2.3%
29
 
2.0%
28
 
2.0%
25
 
1.8%
24
 
1.7%
24
 
1.7%
Other values (227) 1005
70.7%
Common
ValueCountFrequency (%)
) 15
31.2%
( 15
31.2%
, 12
25.0%
· 4
 
8.3%
/ 2
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1422
96.7%
ASCII 44
 
3.0%
None 4
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
115
 
8.1%
64
 
4.5%
38
 
2.7%
38
 
2.7%
32
 
2.3%
29
 
2.0%
28
 
2.0%
25
 
1.8%
24
 
1.7%
24
 
1.7%
Other values (227) 1005
70.7%
ASCII
ValueCountFrequency (%)
) 15
34.1%
( 15
34.1%
, 12
27.3%
/ 2
 
4.5%
None
ValueCountFrequency (%)
· 4
100.0%

구공판_구속
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.958549
Minimum0
Maximum487
Zeros86
Zeros (%)44.6%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:05:15.556256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile41.8
Maximum487
Range487
Interquartile range (IQR)6

Descriptive statistics

Standard deviation47.696942
Coefficient of variation (CV)3.9885224
Kurtosis67.033505
Mean11.958549
Median Absolute Deviation (MAD)1
Skewness7.8224625
Sum2308
Variance2274.9983
MonotonicityNot monotonic
2023-12-13T06:05:15.659068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 86
44.6%
1 20
 
10.4%
2 14
 
7.3%
5 9
 
4.7%
6 8
 
4.1%
14 6
 
3.1%
3 6
 
3.1%
4 5
 
2.6%
9 4
 
2.1%
15 3
 
1.6%
Other values (25) 32
 
16.6%
ValueCountFrequency (%)
0 86
44.6%
1 20
 
10.4%
2 14
 
7.3%
3 6
 
3.1%
4 5
 
2.6%
5 9
 
4.7%
6 8
 
4.1%
8 1
 
0.5%
9 4
 
2.1%
10 2
 
1.0%
ValueCountFrequency (%)
487 1
0.5%
345 1
0.5%
267 1
0.5%
77 1
0.5%
69 1
0.5%
49 2
1.0%
48 1
0.5%
45 1
0.5%
43 1
0.5%
41 1
0.5%

구공판_불구속
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.818653
Minimum0
Maximum2457
Zeros46
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:05:15.762691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q331
95-th percentile275
Maximum2457
Range2457
Interquartile range (IQR)30

Descriptive statistics

Standard deviation218.5457
Coefficient of variation (CV)3.5352711
Kurtosis77.928776
Mean61.818653
Median Absolute Deviation (MAD)6
Skewness7.9154714
Sum11931
Variance47762.222
MonotonicityNot monotonic
2023-12-13T06:05:15.872670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46
23.8%
1 20
 
10.4%
2 13
 
6.7%
4 7
 
3.6%
7 6
 
3.1%
6 6
 
3.1%
3 5
 
2.6%
10 4
 
2.1%
8 4
 
2.1%
16 4
 
2.1%
Other values (63) 78
40.4%
ValueCountFrequency (%)
0 46
23.8%
1 20
10.4%
2 13
 
6.7%
3 5
 
2.6%
4 7
 
3.6%
5 3
 
1.6%
6 6
 
3.1%
7 6
 
3.1%
8 4
 
2.1%
9 1
 
0.5%
ValueCountFrequency (%)
2457 1
0.5%
910 1
0.5%
871 1
0.5%
792 1
0.5%
696 1
0.5%
514 1
0.5%
374 1
0.5%
349 1
0.5%
340 1
0.5%
284 1
0.5%

구약식
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct98
Distinct (%)50.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.53368
Minimum0
Maximum10480
Zeros28
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:05:15.984781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median24
Q383
95-th percentile1028
Maximum10480
Range10480
Interquartile range (IQR)80

Descriptive statistics

Standard deviation861.36977
Coefficient of variation (CV)3.9236338
Kurtosis106.33701
Mean219.53368
Median Absolute Deviation (MAD)24
Skewness9.3752525
Sum42370
Variance741957.88
MonotonicityNot monotonic
2023-12-13T06:05:16.095520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28
 
14.5%
1 11
 
5.7%
2 7
 
3.6%
6 6
 
3.1%
24 5
 
2.6%
5 5
 
2.6%
7 5
 
2.6%
9 4
 
2.1%
12 4
 
2.1%
3 4
 
2.1%
Other values (88) 114
59.1%
ValueCountFrequency (%)
0 28
14.5%
1 11
 
5.7%
2 7
 
3.6%
3 4
 
2.1%
4 1
 
0.5%
5 5
 
2.6%
6 6
 
3.1%
7 5
 
2.6%
8 2
 
1.0%
9 4
 
2.1%
ValueCountFrequency (%)
10480 1
0.5%
2615 1
0.5%
2493 1
0.5%
2425 1
0.5%
2268 1
0.5%
2006 1
0.5%
1672 1
0.5%
1626 1
0.5%
1353 1
0.5%
1076 1
0.5%

소년보호송치
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.108808
Minimum0
Maximum833
Zeros141
Zeros (%)73.1%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:05:16.202300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile30.6
Maximum833
Range833
Interquartile range (IQR)1

Descriptive statistics

Standard deviation70.37869
Coefficient of variation (CV)5.8121896
Kurtosis103.36896
Mean12.108808
Median Absolute Deviation (MAD)0
Skewness9.6093296
Sum2337
Variance4953.16
MonotonicityNot monotonic
2023-12-13T06:05:16.298551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 141
73.1%
1 9
 
4.7%
2 6
 
3.1%
7 5
 
2.6%
3 4
 
2.1%
15 3
 
1.6%
16 2
 
1.0%
9 2
 
1.0%
4 2
 
1.0%
45 2
 
1.0%
Other values (16) 17
 
8.8%
ValueCountFrequency (%)
0 141
73.1%
1 9
 
4.7%
2 6
 
3.1%
3 4
 
2.1%
4 2
 
1.0%
5 1
 
0.5%
6 1
 
0.5%
7 5
 
2.6%
9 2
 
1.0%
10 1
 
0.5%
ValueCountFrequency (%)
833 1
0.5%
428 1
0.5%
199 1
0.5%
144 1
0.5%
138 1
0.5%
100 1
0.5%
55 1
0.5%
45 2
1.0%
33 1
0.5%
29 1
0.5%

가정보호송치
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5958549
Minimum0
Maximum385
Zeros184
Zeros (%)95.3%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:05:16.379031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum385
Range385
Interquartile range (IQR)0

Descriptive statistics

Standard deviation31.313143
Coefficient of variation (CV)8.7081218
Kurtosis122.34179
Mean3.5958549
Median Absolute Deviation (MAD)0
Skewness10.697071
Sum694
Variance980.5129
MonotonicityNot monotonic
2023-12-13T06:05:16.788452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 184
95.3%
2 3
 
1.6%
50 1
 
0.5%
1 1
 
0.5%
385 1
 
0.5%
192 1
 
0.5%
56 1
 
0.5%
4 1
 
0.5%
ValueCountFrequency (%)
0 184
95.3%
1 1
 
0.5%
2 3
 
1.6%
4 1
 
0.5%
50 1
 
0.5%
56 1
 
0.5%
192 1
 
0.5%
385 1
 
0.5%
ValueCountFrequency (%)
385 1
 
0.5%
192 1
 
0.5%
56 1
 
0.5%
50 1
 
0.5%
4 1
 
0.5%
2 3
 
1.6%
1 1
 
0.5%
0 184
95.3%

성매매보호송치
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
191 
1
 
1
76
 
1

Length

Max length2
Median length1
Mean length1.0051813
Min length1

Unique

Unique2 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 191
99.0%
1 1
 
0.5%
76 1
 
0.5%

Length

2023-12-13T06:05:16.923686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:05:17.091199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 191
99.0%
1 1
 
0.5%
76 1
 
0.5%

아동보호송치
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64766839
Minimum0
Maximum109
Zeros187
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:05:17.172311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum109
Range109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.8667422
Coefficient of variation (CV)12.14625
Kurtosis190.35129
Mean0.64766839
Median Absolute Deviation (MAD)0
Skewness13.755527
Sum125
Variance61.885633
MonotonicityNot monotonic
2023-12-13T06:05:17.269959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 187
96.9%
2 2
 
1.0%
8 1
 
0.5%
1 1
 
0.5%
3 1
 
0.5%
109 1
 
0.5%
ValueCountFrequency (%)
0 187
96.9%
1 1
 
0.5%
2 2
 
1.0%
3 1
 
0.5%
8 1
 
0.5%
109 1
 
0.5%
ValueCountFrequency (%)
109 1
 
0.5%
8 1
 
0.5%
3 1
 
0.5%
2 2
 
1.0%
1 1
 
0.5%
0 187
96.9%

기소유예
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct89
Distinct (%)46.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.29534
Minimum0
Maximum4483
Zeros37
Zeros (%)19.2%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:05:17.408909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median14
Q354
95-th percentile623
Maximum4483
Range4483
Interquartile range (IQR)52

Descriptive statistics

Standard deviation484.52411
Coefficient of variation (CV)3.5812329
Kurtosis53.917311
Mean135.29534
Median Absolute Deviation (MAD)14
Skewness6.8805056
Sum26112
Variance234763.62
MonotonicityNot monotonic
2023-12-13T06:05:17.575007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
19.2%
1 10
 
5.2%
2 7
 
3.6%
6 6
 
3.1%
4 6
 
3.1%
13 5
 
2.6%
5 5
 
2.6%
8 4
 
2.1%
17 4
 
2.1%
10 4
 
2.1%
Other values (79) 105
54.4%
ValueCountFrequency (%)
0 37
19.2%
1 10
 
5.2%
2 7
 
3.6%
3 3
 
1.6%
4 6
 
3.1%
5 5
 
2.6%
6 6
 
3.1%
7 3
 
1.6%
8 4
 
2.1%
9 2
 
1.0%
ValueCountFrequency (%)
4483 1
0.5%
3939 1
0.5%
2009 1
0.5%
1358 1
0.5%
1310 1
0.5%
870 1
0.5%
826 1
0.5%
819 1
0.5%
814 1
0.5%
644 1
0.5%

혐의없음
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct93
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.24352
Minimum0
Maximum6838
Zeros21
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:05:17.717191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median14
Q353
95-th percentile515.6
Maximum6838
Range6838
Interquartile range (IQR)50

Descriptive statistics

Standard deviation515.27527
Coefficient of variation (CV)4.4711863
Kurtosis152.80369
Mean115.24352
Median Absolute Deviation (MAD)13
Skewness11.78518
Sum22242
Variance265508.6
MonotonicityNot monotonic
2023-12-13T06:05:17.877478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21
 
10.9%
1 14
 
7.3%
4 9
 
4.7%
2 8
 
4.1%
9 7
 
3.6%
3 6
 
3.1%
15 6
 
3.1%
12 5
 
2.6%
8 5
 
2.6%
11 5
 
2.6%
Other values (83) 107
55.4%
ValueCountFrequency (%)
0 21
10.9%
1 14
7.3%
2 8
 
4.1%
3 6
 
3.1%
4 9
4.7%
5 1
 
0.5%
6 5
 
2.6%
7 2
 
1.0%
8 5
 
2.6%
9 7
 
3.6%
ValueCountFrequency (%)
6838 1
0.5%
1050 1
0.5%
875 1
0.5%
788 1
0.5%
779 1
0.5%
701 1
0.5%
669 1
0.5%
617 1
0.5%
563 1
0.5%
527 1
0.5%

죄가안됨
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3678756
Minimum0
Maximum69
Zeros165
Zeros (%)85.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:05:18.024181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum69
Range69
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.5591008
Coefficient of variation (CV)5.5261608
Kurtosis54.577472
Mean1.3678756
Median Absolute Deviation (MAD)0
Skewness7.2353501
Sum264
Variance57.140004
MonotonicityNot monotonic
2023-12-13T06:05:18.166456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 165
85.5%
1 11
 
5.7%
2 6
 
3.1%
5 3
 
1.6%
3 2
 
1.0%
8 1
 
0.5%
69 1
 
0.5%
45 1
 
0.5%
9 1
 
0.5%
57 1
 
0.5%
ValueCountFrequency (%)
0 165
85.5%
1 11
 
5.7%
2 6
 
3.1%
3 2
 
1.0%
5 3
 
1.6%
8 1
 
0.5%
9 1
 
0.5%
32 1
 
0.5%
45 1
 
0.5%
57 1
 
0.5%
ValueCountFrequency (%)
69 1
 
0.5%
57 1
 
0.5%
45 1
 
0.5%
32 1
 
0.5%
9 1
 
0.5%
8 1
 
0.5%
5 3
 
1.6%
3 2
 
1.0%
2 6
3.1%
1 11
5.7%

공소권없음
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.80829
Minimum0
Maximum8117
Zeros73
Zeros (%)37.8%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:05:18.309619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile156.6
Maximum8117
Range8117
Interquartile range (IQR)8

Descriptive statistics

Standard deviation776.34681
Coefficient of variation (CV)7.3372967
Kurtosis92.409843
Mean105.80829
Median Absolute Deviation (MAD)1
Skewness9.5671103
Sum20421
Variance602714.37
MonotonicityNot monotonic
2023-12-13T06:05:18.471907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 73
37.8%
1 27
 
14.0%
2 15
 
7.8%
4 10
 
5.2%
3 8
 
4.1%
8 5
 
2.6%
5 5
 
2.6%
7 5
 
2.6%
17 4
 
2.1%
13 2
 
1.0%
Other values (36) 39
20.2%
ValueCountFrequency (%)
0 73
37.8%
1 27
 
14.0%
2 15
 
7.8%
3 8
 
4.1%
4 10
 
5.2%
5 5
 
2.6%
6 1
 
0.5%
7 5
 
2.6%
8 5
 
2.6%
9 1
 
0.5%
ValueCountFrequency (%)
8117 1
0.5%
7056 1
0.5%
856 1
0.5%
816 1
0.5%
558 1
0.5%
315 1
0.5%
311 1
0.5%
310 1
0.5%
259 1
0.5%
174 1
0.5%

기소중지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.134715
Minimum0
Maximum1747
Zeros94
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:05:18.629128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile55
Maximum1747
Range1747
Interquartile range (IQR)6

Descriptive statistics

Standard deviation130.64536
Coefficient of variation (CV)6.1815531
Kurtosis160.95006
Mean21.134715
Median Absolute Deviation (MAD)1
Skewness12.278055
Sum4079
Variance17068.211
MonotonicityNot monotonic
2023-12-13T06:05:18.790857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 94
48.7%
1 23
 
11.9%
2 12
 
6.2%
3 8
 
4.1%
6 5
 
2.6%
41 4
 
2.1%
11 3
 
1.6%
4 3
 
1.6%
16 3
 
1.6%
5 3
 
1.6%
Other values (30) 35
 
18.1%
ValueCountFrequency (%)
0 94
48.7%
1 23
 
11.9%
2 12
 
6.2%
3 8
 
4.1%
4 3
 
1.6%
5 3
 
1.6%
6 5
 
2.6%
7 2
 
1.0%
8 1
 
0.5%
10 2
 
1.0%
ValueCountFrequency (%)
1747 1
0.5%
336 1
0.5%
325 1
0.5%
139 1
0.5%
111 1
0.5%
93 1
0.5%
92 1
0.5%
76 1
0.5%
67 1
0.5%
58 1
0.5%

참고인중지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7875648
Minimum0
Maximum158
Zeros143
Zeros (%)74.1%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:05:18.904536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum158
Range158
Interquartile range (IQR)1

Descriptive statistics

Standard deviation11.695862
Coefficient of variation (CV)6.5429023
Kurtosis168.19309
Mean1.7875648
Median Absolute Deviation (MAD)0
Skewness12.639783
Sum345
Variance136.79318
MonotonicityNot monotonic
2023-12-13T06:05:19.026843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 143
74.1%
1 14
 
7.3%
3 12
 
6.2%
2 9
 
4.7%
5 7
 
3.6%
4 2
 
1.0%
7 1
 
0.5%
158 1
 
0.5%
19 1
 
0.5%
31 1
 
0.5%
Other values (2) 2
 
1.0%
ValueCountFrequency (%)
0 143
74.1%
1 14
 
7.3%
2 9
 
4.7%
3 12
 
6.2%
4 2
 
1.0%
5 7
 
3.6%
7 1
 
0.5%
9 1
 
0.5%
10 1
 
0.5%
19 1
 
0.5%
ValueCountFrequency (%)
158 1
 
0.5%
31 1
 
0.5%
19 1
 
0.5%
10 1
 
0.5%
9 1
 
0.5%
7 1
 
0.5%
5 7
3.6%
4 2
 
1.0%
3 12
6.2%
2 9
4.7%

Interactions

2023-12-13T06:05:13.808204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:01.139505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:02.086133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:03.264536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:04.549918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:05.691811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:07.002726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:07.862155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:08.698972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:09.694262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:11.065800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:12.708273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:13.887981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:01.206113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:02.192737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:03.353853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:04.644913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:05.785183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:07.063582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:07.928137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:08.769834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:09.790415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:11.199602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:12.818820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:13.968291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:01.278572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:02.295826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:03.446596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:04.735294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:05.909852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:07.133863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:07.994907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:08.846864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:09.917405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:11.300931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:12.899506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:14.045799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:01.349991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:02.378502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:03.561000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:04.836735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:06.010343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:07.206611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:08.056295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:08.929782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:10.054098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:11.400091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:12.993737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:14.140402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:01.421137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:02.461495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:03.656777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:04.915642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:06.095705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:07.269441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:08.118214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:09.017602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:10.139102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:11.512046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:13.071138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:14.230182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:01.504561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:02.578398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:03.790044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:05.004378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:06.187835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:07.346364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:08.203022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:09.105395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:10.272864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:11.623224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:13.193178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:14.307603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:01.581407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:02.677145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:03.894508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:05.086171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:06.268609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:07.411796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:08.263040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:09.185495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:10.390979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:12.050379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:13.286397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:14.379856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:01.651761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:02.760119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:04.003296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:05.161909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:06.339342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:07.485742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:08.327569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:09.266394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:10.484731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:12.135202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:13.385949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:14.459089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:01.730859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:02.853634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:04.107335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:05.259450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:06.684983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:07.566066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:08.398471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:09.348808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:10.613121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:12.247157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:13.473768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:14.531477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:01.815645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:02.958978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:04.216053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:05.374536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:06.761948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:07.637220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:08.472326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:09.437055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:10.733130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:12.370953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:13.560351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:14.630812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:01.900190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:03.062690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:04.315189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:05.482623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:06.843332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:07.714962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:08.556533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:09.520043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:10.846725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:12.491226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:13.644559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:14.701086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:01.981059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:03.153211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:04.426000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:05.594313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:06.921549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:07.785081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:08.625590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:09.607367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:10.955916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:12.596085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:13.727523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:05:19.135670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구공판_구속구공판_불구속구약식소년보호송치가정보호송치성매매보호송치아동보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지
구공판_구속1.0000.9260.6170.9680.3020.7130.0000.8670.7730.6510.0000.7130.713
구공판_불구속0.9261.0000.6160.9440.5830.4520.0000.8310.7610.6660.5360.7130.713
구약식0.6170.6161.0000.5860.7270.3420.0000.7330.4780.6880.7890.4040.342
소년보호송치0.9680.9440.5861.0000.7180.7130.0000.9340.7770.7830.3610.7130.713
가정보호송치0.3020.5830.7270.7181.0000.0000.0000.8800.3010.9210.8900.0000.000
성매매보호송치0.7130.4520.3420.7130.0001.0000.0000.7950.6330.7970.0000.0000.000
아동보호송치0.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
기소유예0.8670.8310.7330.9340.8800.7950.0001.0000.8730.8940.6930.7610.777
혐의없음0.7730.7610.4780.7770.3010.6330.0000.8731.0000.8710.3740.9410.954
죄가안됨0.6510.6660.6880.7830.9210.7970.0000.8940.8711.0000.8370.0000.000
공소권없음0.0000.5360.7890.3610.8900.0000.0000.6930.3740.8371.0000.3410.000
기소중지0.7130.7130.4040.7130.0000.0000.0000.7610.9410.0000.3411.0000.941
참고인중지0.7130.7130.3420.7130.0000.0000.0000.7770.9540.0000.0000.9411.000
2023-12-13T06:05:19.290505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구공판_구속구공판_불구속구약식소년보호송치가정보호송치아동보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지성매매보호송치
구공판_구속1.0000.7520.2820.4920.2960.2250.4200.4390.3070.3590.4930.4660.696
구공판_불구속0.7521.0000.6070.5550.2860.2270.6380.6930.3960.6020.6800.5510.382
구약식0.2820.6071.0000.4650.1870.1370.8360.7530.3690.6590.6340.4760.330
소년보호송치0.4920.5550.4651.0000.3160.2510.5970.5240.5290.4900.4750.4020.696
가정보호송치0.2960.2860.1870.3161.0000.5350.2590.2530.4230.2640.2130.1120.000
아동보호송치0.2250.2270.1370.2510.5351.0000.1950.2070.2880.1690.1140.1810.000
기소유예0.4200.6380.8360.5970.2590.1951.0000.7710.3970.6270.6320.4950.737
혐의없음0.4390.6930.7530.5240.2530.2070.7711.0000.3960.6570.6300.5610.297
죄가안됨0.3070.3960.3690.5290.4230.2880.3970.3961.0000.3600.3250.3090.474
공소권없음0.3590.6020.6590.4900.2640.1690.6270.6570.3601.0000.6570.4740.000
기소중지0.4930.6800.6340.4750.2130.1140.6320.6300.3250.6571.0000.5920.000
참고인중지0.4660.5510.4760.4020.1120.1810.4950.5610.3090.4740.5921.0000.000
성매매보호송치0.6960.3820.3300.6960.0000.0000.7370.2970.4740.0000.0000.0001.000

Missing values

2023-12-13T06:05:14.805022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:05:14.943534image/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
0절도267910226883301044837888112937
1불법사용0142000580100
2침입절도451102125000721002711
3장물144720345000482217012113
4사기487245726154280003939683823111747158
5컴퓨터등사용사기15303210001664505531
6부당이득0000000230000
7편의시설부정이용019000017270140
8전기통신금융사기피해금환급에관한특별법41212000035904160
9보험사기방지특별법22573000014360062
범죄분류구공판_구속구공판_불구속구약식소년보호송치가정보호송치성매매보호송치아동보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지
183특가법(도주차량)173832000201420720
184특허법00000000110000
185폐기물관리법0128000013580200
186풍속영업의규제에관한법률02140000110720
187하천법0060000010000
188학교보건법00160000680410
189학원의설립운영및과외교습에관한법률00120000730000
190화물자동차운수사업법0061000019210120
191화학물질관리법81720001210000
192기타특별법49349167272028197793271393