Overview

Dataset statistics

Number of variables14
Number of observations187
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.0 KiB
Average record size in memory125.7 B

Variable types

Text1
Numeric11
Categorical2

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 창원지방검찰청의 범죄자 처분결과에 관한 통계임.
Author대검찰청
URLhttps://www.data.go.kr/data/15084994/fileData.do

Alerts

구공판_구속 is highly overall correlated with 구공판_불구속High correlation
구공판_불구속 is highly overall correlated with 구공판_구속 and 5 other fieldsHigh correlation
구약식 is highly overall correlated with 구공판_불구속 and 4 other fieldsHigh correlation
가정보호송치 is highly overall correlated with 아동보호송치High correlation
기소유예 is highly overall correlated with 구공판_불구속 and 5 other fieldsHigh correlation
혐의없음 is highly overall correlated with 구공판_불구속 and 5 other fieldsHigh 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 기소유예 and 2 other fieldsHigh correlation
아동보호송치 is highly overall correlated with 가정보호송치High correlation
성매매보호송치 is highly imbalanced (95.2%)Imbalance
아동보호송치 is highly imbalanced (90.1%)Imbalance
범죄분류 has unique valuesUnique
구공판_구속 has 92 (49.2%) zerosZeros
구공판_불구속 has 46 (24.6%) zerosZeros
구약식 has 33 (17.6%) zerosZeros
소년보호송치 has 140 (74.9%) zerosZeros
가정보호송치 has 173 (92.5%) zerosZeros
기소유예 has 30 (16.0%) zerosZeros
혐의없음 has 27 (14.4%) zerosZeros
죄가안됨 has 163 (87.2%) zerosZeros
공소권없음 has 84 (44.9%) zerosZeros
기소중지 has 103 (55.1%) zerosZeros
참고인중지 has 153 (81.8%) zerosZeros

Reproduction

Analysis started2023-12-12 21:22:02.355227
Analysis finished2023-12-12 21:22:15.395405
Duration13.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct187
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-13T06:22:15.570113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length7.4812834
Min length2

Characters and Unicode

Total characters1399
Distinct characters240
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

Unique187 ?
Unique (%)100.0%

Sample

1st row절도
2nd row불법사용
3rd row침입절도
4th row장물
5th row사기
ValueCountFrequency (%)
절도 1
 
0.5%
부동산실권리자명의등기에관한법률 1
 
0.5%
성매매알선등행위의처벌에관한법률 1
 
0.5%
마약류관리에관한법률(대마 1
 
0.5%
마약류관리에관한법률(마약 1
 
0.5%
마약류관리에관한법률(향정 1
 
0.5%
물환경보전법 1
 
0.5%
범죄수익은닉의규제및처벌등에관한법률 1
 
0.5%
변호사법 1
 
0.5%
병역법 1
 
0.5%
Other values (177) 177
94.7%
2023-12-13T06:22:15.927155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
112
 
8.0%
62
 
4.4%
35
 
2.5%
34
 
2.4%
30
 
2.1%
29
 
2.1%
24
 
1.7%
23
 
1.6%
22
 
1.6%
21
 
1.5%
Other values (230) 1007
72.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1354
96.8%
Other Punctuation 17
 
1.2%
Close Punctuation 14
 
1.0%
Open Punctuation 14
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
112
 
8.3%
62
 
4.6%
35
 
2.6%
34
 
2.5%
30
 
2.2%
29
 
2.1%
24
 
1.8%
23
 
1.7%
22
 
1.6%
21
 
1.6%
Other values (225) 962
71.0%
Other Punctuation
ValueCountFrequency (%)
, 12
70.6%
· 4
 
23.5%
/ 1
 
5.9%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1354
96.8%
Common 45
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
112
 
8.3%
62
 
4.6%
35
 
2.6%
34
 
2.5%
30
 
2.2%
29
 
2.1%
24
 
1.8%
23
 
1.7%
22
 
1.6%
21
 
1.6%
Other values (225) 962
71.0%
Common
ValueCountFrequency (%)
) 14
31.1%
( 14
31.1%
, 12
26.7%
· 4
 
8.9%
/ 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1354
96.8%
ASCII 41
 
2.9%
None 4
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
112
 
8.3%
62
 
4.6%
35
 
2.6%
34
 
2.5%
30
 
2.2%
29
 
2.1%
24
 
1.8%
23
 
1.7%
22
 
1.6%
21
 
1.6%
Other values (225) 962
71.0%
ASCII
ValueCountFrequency (%)
) 14
34.1%
( 14
34.1%
, 12
29.3%
/ 1
 
2.4%
None
ValueCountFrequency (%)
· 4
100.0%

구공판_구속
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3529412
Minimum0
Maximum265
Zeros92
Zeros (%)49.2%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:22:16.112580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile38.5
Maximum265
Range265
Interquartile range (IQR)5

Descriptive statistics

Standard deviation31.496262
Coefficient of variation (CV)3.3675249
Kurtosis41.611536
Mean9.3529412
Median Absolute Deviation (MAD)1
Skewness6.1480096
Sum1749
Variance992.01455
MonotonicityNot monotonic
2023-12-13T06:22:16.312384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 92
49.2%
1 18
 
9.6%
2 17
 
9.1%
3 8
 
4.3%
5 7
 
3.7%
6 4
 
2.1%
10 3
 
1.6%
13 2
 
1.1%
4 2
 
1.1%
29 2
 
1.1%
Other values (27) 32
 
17.1%
ValueCountFrequency (%)
0 92
49.2%
1 18
 
9.6%
2 17
 
9.1%
3 8
 
4.3%
4 2
 
1.1%
5 7
 
3.7%
6 4
 
2.1%
7 1
 
0.5%
8 2
 
1.1%
9 2
 
1.1%
ValueCountFrequency (%)
265 1
0.5%
219 1
0.5%
209 1
0.5%
107 1
0.5%
76 1
0.5%
53 1
0.5%
51 1
0.5%
46 1
0.5%
43 1
0.5%
40 1
0.5%

구공판_불구속
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.197861
Minimum0
Maximum1679
Zeros46
Zeros (%)24.6%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:22:16.488264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q325
95-th percentile250.6
Maximum1679
Range1679
Interquartile range (IQR)24

Descriptive statistics

Standard deviation191.03656
Coefficient of variation (CV)3.3993564
Kurtosis48.744009
Mean56.197861
Median Absolute Deviation (MAD)4
Skewness6.5512259
Sum10509
Variance36494.966
MonotonicityNot monotonic
2023-12-13T06:22:16.657873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46
24.6%
1 17
 
9.1%
2 14
 
7.5%
3 12
 
6.4%
11 5
 
2.7%
9 5
 
2.7%
6 5
 
2.7%
4 5
 
2.7%
23 4
 
2.1%
63 3
 
1.6%
Other values (54) 71
38.0%
ValueCountFrequency (%)
0 46
24.6%
1 17
 
9.1%
2 14
 
7.5%
3 12
 
6.4%
4 5
 
2.7%
5 3
 
1.6%
6 5
 
2.7%
7 3
 
1.6%
8 1
 
0.5%
9 5
 
2.7%
ValueCountFrequency (%)
1679 1
0.5%
1550 1
0.5%
827 1
0.5%
557 1
0.5%
555 1
0.5%
404 1
0.5%
316 1
0.5%
305 1
0.5%
301 1
0.5%
268 1
0.5%

구약식
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct92
Distinct (%)49.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.01604
Minimum0
Maximum12359
Zeros33
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:22:16.837694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median15
Q392.5
95-th percentile701
Maximum12359
Range12359
Interquartile range (IQR)90.5

Descriptive statistics

Standard deviation958.62901
Coefficient of variation (CV)4.8905641
Kurtosis141.23153
Mean196.01604
Median Absolute Deviation (MAD)15
Skewness11.285024
Sum36655
Variance918969.59
MonotonicityNot monotonic
2023-12-13T06:22:16.999688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33
 
17.6%
2 10
 
5.3%
1 9
 
4.8%
6 6
 
3.2%
3 6
 
3.2%
14 4
 
2.1%
10 4
 
2.1%
20 4
 
2.1%
47 4
 
2.1%
11 3
 
1.6%
Other values (82) 104
55.6%
ValueCountFrequency (%)
0 33
17.6%
1 9
 
4.8%
2 10
 
5.3%
3 6
 
3.2%
4 2
 
1.1%
5 3
 
1.6%
6 6
 
3.2%
7 3
 
1.6%
8 2
 
1.1%
9 2
 
1.1%
ValueCountFrequency (%)
12359 1
0.5%
2384 1
0.5%
2373 1
0.5%
1693 1
0.5%
1546 1
0.5%
1374 1
0.5%
1297 1
0.5%
1078 1
0.5%
780 1
0.5%
737 1
0.5%

소년보호송치
Real number (ℝ)

ZEROS 

Distinct22
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4545455
Minimum0
Maximum311
Zeros140
Zeros (%)74.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:22:17.123199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile14.7
Maximum311
Range311
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation27.692665
Coefficient of variation (CV)5.0769885
Kurtosis85.407636
Mean5.4545455
Median Absolute Deviation (MAD)0
Skewness8.6133436
Sum1020
Variance766.88368
MonotonicityNot monotonic
2023-12-13T06:22:17.230137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 140
74.9%
1 10
 
5.3%
5 5
 
2.7%
3 4
 
2.1%
6 3
 
1.6%
2 3
 
1.6%
7 2
 
1.1%
9 2
 
1.1%
10 2
 
1.1%
76 2
 
1.1%
Other values (12) 14
 
7.5%
ValueCountFrequency (%)
0 140
74.9%
1 10
 
5.3%
2 3
 
1.6%
3 4
 
2.1%
4 1
 
0.5%
5 5
 
2.7%
6 3
 
1.6%
7 2
 
1.1%
8 1
 
0.5%
9 2
 
1.1%
ValueCountFrequency (%)
311 1
0.5%
153 1
0.5%
112 1
0.5%
76 2
1.1%
34 1
0.5%
22 1
0.5%
20 1
0.5%
15 2
1.1%
14 2
1.1%
12 1
0.5%

가정보호송치
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8609626
Minimum0
Maximum498
Zeros173
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:22:17.356895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum498
Range498
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.724249
Coefficient of variation (CV)8.3778157
Kurtosis121.47365
Mean4.8609626
Median Absolute Deviation (MAD)0
Skewness10.610285
Sum909
Variance1658.4644
MonotonicityNot monotonic
2023-12-13T06:22:17.467042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 173
92.5%
1 5
 
2.7%
2 3
 
1.6%
78 1
 
0.5%
498 1
 
0.5%
8 1
 
0.5%
228 1
 
0.5%
82 1
 
0.5%
4 1
 
0.5%
ValueCountFrequency (%)
0 173
92.5%
1 5
 
2.7%
2 3
 
1.6%
4 1
 
0.5%
8 1
 
0.5%
78 1
 
0.5%
82 1
 
0.5%
228 1
 
0.5%
498 1
 
0.5%
ValueCountFrequency (%)
498 1
 
0.5%
228 1
 
0.5%
82 1
 
0.5%
78 1
 
0.5%
8 1
 
0.5%
4 1
 
0.5%
2 3
 
1.6%
1 5
 
2.7%
0 173
92.5%

성매매보호송치
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
186 
53
 
1

Length

Max length2
Median length1
Mean length1.0053476
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
0 186
99.5%
53 1
 
0.5%

Length

2023-12-13T06:22:17.593012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:22:17.719170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 186
99.5%
53 1
 
0.5%

아동보호송치
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
182 
2
 
2
8
 
1
3
 
1
103
 
1

Length

Max length3
Median length1
Mean length1.0106952
Min length1

Unique

Unique3 ?
Unique (%)1.6%

Sample

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

Common Values

ValueCountFrequency (%)
0 182
97.3%
2 2
 
1.1%
8 1
 
0.5%
3 1
 
0.5%
103 1
 
0.5%

Length

2023-12-13T06:22:17.822167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:22:17.950265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 182
97.3%
2 2
 
1.1%
8 1
 
0.5%
3 1
 
0.5%
103 1
 
0.5%

기소유예
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.620321
Minimum0
Maximum2694
Zeros30
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:22:18.089451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q341
95-th percentile373.1
Maximum2694
Range2694
Interquartile range (IQR)39

Descriptive statistics

Standard deviation301.89484
Coefficient of variation (CV)3.2950642
Kurtosis43.785574
Mean91.620321
Median Absolute Deviation (MAD)8
Skewness6.2005388
Sum17133
Variance91140.495
MonotonicityNot monotonic
2023-12-13T06:22:18.226674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30
 
16.0%
2 15
 
8.0%
1 13
 
7.0%
3 12
 
6.4%
8 8
 
4.3%
6 6
 
3.2%
4 5
 
2.7%
5 5
 
2.7%
15 5
 
2.7%
32 4
 
2.1%
Other values (63) 84
44.9%
ValueCountFrequency (%)
0 30
16.0%
1 13
7.0%
2 15
8.0%
3 12
 
6.4%
4 5
 
2.7%
5 5
 
2.7%
6 6
 
3.2%
7 1
 
0.5%
8 8
 
4.3%
9 2
 
1.1%
ValueCountFrequency (%)
2694 1
0.5%
2061 1
0.5%
1792 1
0.5%
816 1
0.5%
784 1
0.5%
684 1
0.5%
636 1
0.5%
518 1
0.5%
469 1
0.5%
386 1
0.5%

혐의없음
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct82
Distinct (%)43.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.566845
Minimum0
Maximum5316
Zeros27
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:22:18.373419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median13
Q345
95-th percentile382.7
Maximum5316
Range5316
Interquartile range (IQR)42

Descriptive statistics

Standard deviation404.96234
Coefficient of variation (CV)4.572392
Kurtosis151.23939
Mean88.566845
Median Absolute Deviation (MAD)12
Skewness11.771919
Sum16562
Variance163994.49
MonotonicityNot monotonic
2023-12-13T06:22:18.504958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27
 
14.4%
3 9
 
4.8%
6 9
 
4.8%
1 9
 
4.8%
4 8
 
4.3%
2 8
 
4.3%
5 7
 
3.7%
13 4
 
2.1%
7 4
 
2.1%
15 4
 
2.1%
Other values (72) 98
52.4%
ValueCountFrequency (%)
0 27
14.4%
1 9
 
4.8%
2 8
 
4.3%
3 9
 
4.8%
4 8
 
4.3%
5 7
 
3.7%
6 9
 
4.8%
7 4
 
2.1%
8 4
 
2.1%
9 1
 
0.5%
ValueCountFrequency (%)
5316 1
0.5%
762 1
0.5%
742 1
0.5%
590 1
0.5%
485 1
0.5%
479 1
0.5%
474 1
0.5%
456 1
0.5%
435 1
0.5%
383 1
0.5%

죄가안됨
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64705882
Minimum0
Maximum29
Zeros163
Zeros (%)87.2%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:22:18.627207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.7
Maximum29
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.5655957
Coefficient of variation (CV)5.510466
Kurtosis53.608889
Mean0.64705882
Median Absolute Deviation (MAD)0
Skewness7.2683209
Sum121
Variance12.713472
MonotonicityNot monotonic
2023-12-13T06:22:18.718836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 163
87.2%
1 14
 
7.5%
2 4
 
2.1%
29 2
 
1.1%
3 1
 
0.5%
25 1
 
0.5%
9 1
 
0.5%
4 1
 
0.5%
ValueCountFrequency (%)
0 163
87.2%
1 14
 
7.5%
2 4
 
2.1%
3 1
 
0.5%
4 1
 
0.5%
9 1
 
0.5%
25 1
 
0.5%
29 2
 
1.1%
ValueCountFrequency (%)
29 2
 
1.1%
25 1
 
0.5%
9 1
 
0.5%
4 1
 
0.5%
3 1
 
0.5%
2 4
 
2.1%
1 14
 
7.5%
0 163
87.2%

공소권없음
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.524064
Minimum0
Maximum6339
Zeros84
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:22:18.873398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile121.7
Maximum6339
Range6339
Interquartile range (IQR)4

Descriptive statistics

Standard deviation637.26309
Coefficient of variation (CV)7.2810043
Kurtosis87.486444
Mean87.524064
Median Absolute Deviation (MAD)1
Skewness9.3204667
Sum16367
Variance406104.24
MonotonicityNot monotonic
2023-12-13T06:22:19.026332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 84
44.9%
1 31
 
16.6%
3 10
 
5.3%
2 9
 
4.8%
4 9
 
4.8%
7 4
 
2.1%
17 3
 
1.6%
9 3
 
1.6%
5 3
 
1.6%
8 2
 
1.1%
Other values (25) 29
 
15.5%
ValueCountFrequency (%)
0 84
44.9%
1 31
 
16.6%
2 9
 
4.8%
3 10
 
5.3%
4 9
 
4.8%
5 3
 
1.6%
6 2
 
1.1%
7 4
 
2.1%
8 2
 
1.1%
9 3
 
1.6%
ValueCountFrequency (%)
6339 1
0.5%
5925 1
0.5%
913 1
0.5%
521 1
0.5%
458 1
0.5%
326 1
0.5%
308 1
0.5%
221 1
0.5%
214 1
0.5%
128 1
0.5%

기소중지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.010695
Minimum0
Maximum1253
Zeros103
Zeros (%)55.1%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:22:19.192072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile56
Maximum1253
Range1253
Interquartile range (IQR)5

Descriptive statistics

Standard deviation99.32709
Coefficient of variation (CV)5.5148949
Kurtosis131.16011
Mean18.010695
Median Absolute Deviation (MAD)0
Skewness10.87955
Sum3368
Variance9865.8709
MonotonicityNot monotonic
2023-12-13T06:22:19.712245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 103
55.1%
1 20
 
10.7%
2 7
 
3.7%
5 5
 
2.7%
4 4
 
2.1%
6 4
 
2.1%
12 3
 
1.6%
7 3
 
1.6%
3 3
 
1.6%
56 2
 
1.1%
Other values (27) 33
 
17.6%
ValueCountFrequency (%)
0 103
55.1%
1 20
 
10.7%
2 7
 
3.7%
3 3
 
1.6%
4 4
 
2.1%
5 5
 
2.7%
6 4
 
2.1%
7 3
 
1.6%
8 1
 
0.5%
11 1
 
0.5%
ValueCountFrequency (%)
1253 1
0.5%
393 1
0.5%
298 1
0.5%
155 1
0.5%
109 1
0.5%
97 1
0.5%
67 1
0.5%
63 1
0.5%
57 1
0.5%
56 2
1.1%

참고인중지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1925134
Minimum0
Maximum123
Zeros153
Zeros (%)81.8%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:22:19.849746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum123
Range123
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.1048461
Coefficient of variation (CV)7.6350055
Kurtosis174.84009
Mean1.1925134
Median Absolute Deviation (MAD)0
Skewness13.030596
Sum223
Variance82.898223
MonotonicityNot monotonic
2023-12-13T06:22:19.954133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 153
81.8%
1 13
 
7.0%
3 7
 
3.7%
2 6
 
3.2%
10 2
 
1.1%
5 2
 
1.1%
123 1
 
0.5%
11 1
 
0.5%
6 1
 
0.5%
7 1
 
0.5%
ValueCountFrequency (%)
0 153
81.8%
1 13
 
7.0%
2 6
 
3.2%
3 7
 
3.7%
5 2
 
1.1%
6 1
 
0.5%
7 1
 
0.5%
10 2
 
1.1%
11 1
 
0.5%
123 1
 
0.5%
ValueCountFrequency (%)
123 1
 
0.5%
11 1
 
0.5%
10 2
 
1.1%
7 1
 
0.5%
6 1
 
0.5%
5 2
 
1.1%
3 7
 
3.7%
2 6
 
3.2%
1 13
 
7.0%
0 153
81.8%

Interactions

2023-12-13T06:22:14.288272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:02.908447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:04.010173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:05.162991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:06.196192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:07.591695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:08.542625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:09.537176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:11.163132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:12.285700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:13.202286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:14.358297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:02.992765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:04.103189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:05.242517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:06.286324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:07.662074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:08.612364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:09.665995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:11.291509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:12.378651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:13.278091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:14.438702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:03.111333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:04.227637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:05.355716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:06.408320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:07.755171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:08.694828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:09.808928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:11.462170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:12.466680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:13.365286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:14.523080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:03.193622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:04.367458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:05.438233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:06.510342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:07.853455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:08.814655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:09.987627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:11.571626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:12.545432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:13.437462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:14.619670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:03.293984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:04.466595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:05.532008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:06.608743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:07.951835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:08.892120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:10.186989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:11.670600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:12.621333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:13.515583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:14.697528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:03.391644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:04.579789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:05.628059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:06.697658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:08.019036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:08.992769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:10.324996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:11.768743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:12.703757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:13.591682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:14.783314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:03.507498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:04.660568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:05.725134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:06.803536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:08.090470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:09.067703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:10.457688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:11.852984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:12.778911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:13.664263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:14.851575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:03.629443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:04.758788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:05.813286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:06.908301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:08.162645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:09.141103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:10.593065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:11.929833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:12.860292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:13.993926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:14.928409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:03.706915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:04.859882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:05.948261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:06.993856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:08.241197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:09.224281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:10.736325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:12.009120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:12.946205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:14.065166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:15.007774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:03.787298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:04.965568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:06.036282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:07.118938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:08.318847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:09.302483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:10.899431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:12.100115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:13.033408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:14.137922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:15.080988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:03.909707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:05.071737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:06.113289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:07.213755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:08.426192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:09.413777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:11.024633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:12.190215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:13.117841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:22:14.209481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:22:20.039556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구공판_구속구공판_불구속구약식소년보호송치가정보호송치성매매보호송치아동보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지
구공판_구속1.0000.8750.5480.8840.6920.2590.2680.9650.8380.4510.0000.6341.000
구공판_불구속0.8751.0000.9480.9840.7680.0000.2690.8780.9150.5110.7930.5100.882
구약식0.5480.9481.0000.9050.3960.0000.3120.6900.5940.3870.8180.4490.240
소년보호송치0.8840.9840.9051.0000.8370.0000.4400.9320.9760.6180.8850.6191.000
가정보호송치0.6920.7680.3960.8371.0000.0000.7520.8910.0000.7970.4880.0000.000
성매매보호송치0.2590.0000.0000.0000.0001.0000.0000.3420.0000.0000.0000.0000.000
아동보호송치0.2680.2690.3120.4400.7520.0001.0000.6530.0000.8480.5410.0000.000
기소유예0.9650.8780.6900.9320.8910.3420.6531.0000.8600.8090.6190.6341.000
혐의없음0.8380.9150.5940.9760.0000.0000.0000.8601.0000.0000.0000.7121.000
죄가안됨0.4510.5110.3870.6180.7970.0000.8480.8090.0001.0000.5410.0000.000
공소권없음0.0000.7930.8180.8850.4880.0000.5410.6190.0000.5411.0000.7120.000
기소중지0.6340.5100.4490.6190.0000.0000.0000.6340.7120.0000.7121.0001.000
참고인중지1.0000.8820.2401.0000.0000.0000.0001.0001.0000.0000.0001.0001.000
2023-12-13T06:22:20.169865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아동보호송치성매매보호송치
아동보호송치1.0000.000
성매매보호송치0.0001.000
2023-12-13T06:22:20.270240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구공판_구속구공판_불구속구약식소년보호송치가정보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지성매매보호송치아동보호송치
구공판_구속1.0000.6800.1870.4450.3340.2300.2920.3770.3860.4000.2970.2510.000
구공판_불구속0.6801.0000.5770.4890.3200.5850.6510.3930.5880.6270.4490.0000.184
구약식0.1870.5771.0000.3660.2240.8110.7310.3780.5830.5930.4250.0000.246
소년보호송치0.4450.4890.3661.0000.4060.4620.4140.3330.4150.4540.3390.0000.316
가정보호송치0.3340.3200.2240.4061.0000.3090.3060.4580.3450.2740.1740.0000.697
기소유예0.2300.5850.8110.4620.3091.0000.7260.3830.5870.6150.5120.3610.492
혐의없음0.2920.6510.7310.4140.3060.7261.0000.4460.6290.6950.5390.0000.000
죄가안됨0.3770.3930.3780.3330.4580.3830.4461.0000.3820.4230.3350.0000.483
공소권없음0.3860.5880.5830.4150.3450.5870.6290.3821.0000.6410.4100.0000.479
기소중지0.4000.6270.5930.4540.2740.6150.6950.4230.6411.0000.5650.0000.000
참고인중지0.2970.4490.4250.3390.1740.5120.5390.3350.4100.5651.0000.0000.000
성매매보호송치0.2510.0000.0000.0000.0000.3610.0000.0000.0000.0000.0001.0000.000
아동보호송치0.0000.1840.2460.3160.6970.4920.0000.4830.4790.0000.0000.0001.000

Missing values

2023-12-13T06:22:15.187874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:22:15.335422image/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
0절도2194047803111002061590260672
1불법사용036140001500010
2침입절도46100560004480910
3장물0414100016240001
4사기265167915461120002694531612141253123
5컴퓨터등사용사기3221920001823904222
6부당이득0000000020000
7편의시설부정이용007000013930750
8전기통신금융사기피해금환급에관한특별법0111200002694103570
9보험사기방지특별법14100000860050
범죄분류구공판_구속구공판_불구속구약식소년보호송치가정보호송치성매매보호송치아동보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지
177특가법(도주차량)15804750008701910
178특허법0000000030300
179폐기물관리법015147000040170112
180풍속영업의규제에관한법률0000000060100
181하천법00200000580000
182학원의설립운영및과외교습에관한법률00900003330000
183화물자동차운수사업법0054000019140110
184화재예방,소방시설설치유지및안전관리에관한법률0000000100000
185화학물질관리법068790004140000
186기타특별법5331610783202469479257280