Overview

Dataset statistics

Number of variables14
Number of observations176
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.6 KiB
Average record size in memory125.8 B

Variable types

Text1
Numeric11
Categorical2

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 서울남부지방검찰청의 범죄자 처분결과에 관한 통계임.
Author대검찰청
URLhttps://www.data.go.kr/data/15084899/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
소년보호송치 is highly overall correlated with 기소유예 and 2 other fieldsHigh correlation
가정보호송치 is highly overall correlated with 아동보호송치High 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 아동보호송치High correlation
공소권없음 is highly overall correlated with 구공판_불구속 and 5 other fieldsHigh correlation
기소중지 is highly overall correlated with 구공판_불구속 and 5 other fieldsHigh correlation
참고인중지 is highly overall correlated with 기소중지High correlation
아동보호송치 is highly overall correlated with 소년보호송치 and 4 other fieldsHigh correlation
성매매보호송치 is highly imbalanced (94.9%)Imbalance
아동보호송치 is highly imbalanced (93.6%)Imbalance
범죄분류 has unique valuesUnique
구공판_구속 has 91 (51.7%) zerosZeros
구공판_불구속 has 53 (30.1%) zerosZeros
구약식 has 32 (18.2%) zerosZeros
소년보호송치 has 131 (74.4%) zerosZeros
가정보호송치 has 163 (92.6%) zerosZeros
기소유예 has 48 (27.3%) zerosZeros
혐의없음 has 26 (14.8%) zerosZeros
죄가안됨 has 154 (87.5%) zerosZeros
공소권없음 has 82 (46.6%) zerosZeros
기소중지 has 89 (50.6%) zerosZeros
참고인중지 has 138 (78.4%) zerosZeros

Reproduction

Analysis started2023-12-12 16:47:56.644338
Analysis finished2023-12-12 16:48:11.058725
Duration14.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct176
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-13T01:48:11.241912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length23
Mean length8.2102273
Min length2

Characters and Unicode

Total characters1445
Distinct characters236
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

Unique176 ?
Unique (%)100.0%

Sample

1st row절도
2nd row불법사용
3rd row침입절도
4th row장물
5th row사기
ValueCountFrequency (%)
관한법률 23
 
8.0%
및 20
 
6.9%
마약류관리에 3
 
1.0%
처벌등에 2
 
0.7%
규제에 2
 
0.7%
관리에 2
 
0.7%
이용에 2
 
0.7%
성보호에 2
 
0.7%
아동·청소년의 2
 
0.7%
안전관리에 2
 
0.7%
Other values (225) 228
79.2%
2023-12-13T01:48:11.923522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
112
 
7.8%
법 103
 
7.1%
관 57
 
3.9%
한 34
 
2.4%
에 33
 
2.3%
률 29
 
2.0%
리 25
 
1.7%
사 24
 
1.7%
의 23
 
1.6%
보 22
 
1.5%
Other values (226) 983
68.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1288
89.1%
Space Separator 112
 
7.8%
Other Punctuation 17
 
1.2%
Close Punctuation 14
 
1.0%
Open Punctuation 14
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
법 103
 
8.0%
관 57
 
4.4%
한 34
 
2.6%
에 33
 
2.6%
률 29
 
2.3%
리 25
 
1.9%
사 24
 
1.9%
의 23
 
1.8%
보 22
 
1.7%
등 21
 
1.6%
Other values (220) 917
71.2%
Other Punctuation
ValueCountFrequency (%)
, 12
70.6%
· 4
 
23.5%
/ 1
 
5.9%
Space Separator
ValueCountFrequency (%)
112
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1288
89.1%
Common 157
 
10.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
법 103
 
8.0%
관 57
 
4.4%
한 34
 
2.6%
에 33
 
2.6%
률 29
 
2.3%
리 25
 
1.9%
사 24
 
1.9%
의 23
 
1.8%
보 22
 
1.7%
등 21
 
1.6%
Other values (220) 917
71.2%
Common
ValueCountFrequency (%)
112
71.3%
) 14
 
8.9%
( 14
 
8.9%
, 12
 
7.6%
· 4
 
2.5%
/ 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1288
89.1%
ASCII 153
 
10.6%
None 4
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
112
73.2%
) 14
 
9.2%
( 14
 
9.2%
, 12
 
7.8%
/ 1
 
0.7%
Hangul
ValueCountFrequency (%)
법 103
 
8.0%
관 57
 
4.4%
한 34
 
2.6%
에 33
 
2.6%
률 29
 
2.3%
리 25
 
1.9%
사 24
 
1.9%
의 23
 
1.8%
보 22
 
1.7%
등 21
 
1.6%
Other values (220) 917
71.2%
None
ValueCountFrequency (%)
· 4
100.0%

구공판_구속
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4886364
Minimum0
Maximum321
Zeros91
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T01:48:12.063969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile32
Maximum321
Range321
Interquartile range (IQR)3

Descriptive statistics

Standard deviation29.264115
Coefficient of variation (CV)3.9078029
Kurtosis80.132959
Mean7.4886364
Median Absolute Deviation (MAD)0
Skewness8.2351788
Sum1318
Variance856.38844
MonotonicityNot monotonic
2023-12-13T01:48:12.225010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 91
51.7%
1 22
 
12.5%
2 13
 
7.4%
3 8
 
4.5%
4 5
 
2.8%
6 4
 
2.3%
9 4
 
2.3%
8 3
 
1.7%
5 2
 
1.1%
32 2
 
1.1%
Other values (20) 22
 
12.5%
ValueCountFrequency (%)
0 91
51.7%
1 22
 
12.5%
2 13
 
7.4%
3 8
 
4.5%
4 5
 
2.8%
5 2
 
1.1%
6 4
 
2.3%
7 2
 
1.1%
8 3
 
1.7%
9 4
 
2.3%
ValueCountFrequency (%)
321 1
0.6%
161 1
0.6%
102 1
0.6%
59 1
0.6%
53 1
0.6%
52 1
0.6%
49 1
0.6%
37 1
0.6%
32 2
1.1%
29 1
0.6%

구공판_불구속
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.136364
Minimum0
Maximum1352
Zeros53
Zeros (%)30.1%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T01:48:12.362838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q314.25
95-th percentile156.75
Maximum1352
Range1352
Interquartile range (IQR)14.25

Descriptive statistics

Standard deviation122.24511
Coefficient of variation (CV)3.4791623
Kurtosis79.061663
Mean35.136364
Median Absolute Deviation (MAD)2
Skewness7.9819847
Sum6184
Variance14943.867
MonotonicityNot monotonic
2023-12-13T01:48:12.513047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 53
30.1%
1 24
13.6%
2 14
 
8.0%
9 5
 
2.8%
5 5
 
2.8%
6 5
 
2.8%
13 4
 
2.3%
3 4
 
2.3%
4 4
 
2.3%
11 4
 
2.3%
Other values (41) 54
30.7%
ValueCountFrequency (%)
0 53
30.1%
1 24
13.6%
2 14
 
8.0%
3 4
 
2.3%
4 4
 
2.3%
5 5
 
2.8%
6 5
 
2.8%
7 2
 
1.1%
8 3
 
1.7%
9 5
 
2.8%
ValueCountFrequency (%)
1352 1
0.6%
517 1
0.6%
425 1
0.6%
316 1
0.6%
288 1
0.6%
267 1
0.6%
223 1
0.6%
205 1
0.6%
195 1
0.6%
144 1
0.6%

구약식
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.67045
Minimum0
Maximum4515
Zeros32
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T01:48:12.679919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median10.5
Q355.75
95-th percentile446
Maximum4515
Range4515
Interquartile range (IQR)54.75

Descriptive statistics

Standard deviation408.82995
Coefficient of variation (CV)3.4162981
Kurtosis78.042219
Mean119.67045
Median Absolute Deviation (MAD)10.5
Skewness7.8957196
Sum21062
Variance167141.93
MonotonicityNot monotonic
2023-12-13T01:48:12.823753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32
 
18.2%
1 21
 
11.9%
2 15
 
8.5%
5 6
 
3.4%
11 5
 
2.8%
4 5
 
2.8%
20 3
 
1.7%
15 3
 
1.7%
13 3
 
1.7%
26 3
 
1.7%
Other values (69) 80
45.5%
ValueCountFrequency (%)
0 32
18.2%
1 21
11.9%
2 15
8.5%
3 2
 
1.1%
4 5
 
2.8%
5 6
 
3.4%
7 2
 
1.1%
8 2
 
1.1%
9 1
 
0.6%
10 2
 
1.1%
ValueCountFrequency (%)
4515 1
0.6%
1442 1
0.6%
1421 1
0.6%
1294 1
0.6%
1038 1
0.6%
1025 1
0.6%
955 1
0.6%
682 1
0.6%
479 1
0.6%
435 1
0.6%

소년보호송치
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4090909
Minimum0
Maximum240
Zeros131
Zeros (%)74.4%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T01:48:12.960036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile10.5
Maximum240
Range240
Interquartile range (IQR)1

Descriptive statistics

Standard deviation21.80623
Coefficient of variation (CV)4.945743
Kurtosis82.033833
Mean4.4090909
Median Absolute Deviation (MAD)0
Skewness8.3788847
Sum776
Variance475.51169
MonotonicityNot monotonic
2023-12-13T01:48:13.113834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 131
74.4%
1 10
 
5.7%
3 7
 
4.0%
2 5
 
2.8%
4 4
 
2.3%
6 3
 
1.7%
10 2
 
1.1%
64 2
 
1.1%
5 2
 
1.1%
240 1
 
0.6%
Other values (9) 9
 
5.1%
ValueCountFrequency (%)
0 131
74.4%
1 10
 
5.7%
2 5
 
2.8%
3 7
 
4.0%
4 4
 
2.3%
5 2
 
1.1%
6 3
 
1.7%
7 1
 
0.6%
8 1
 
0.6%
9 1
 
0.6%
ValueCountFrequency (%)
240 1
0.6%
111 1
0.6%
66 1
0.6%
64 2
1.1%
48 1
0.6%
29 1
0.6%
13 1
0.6%
12 1
0.6%
10 2
1.1%
9 1
0.6%

가정보호송치
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8977273
Minimum0
Maximum419
Zeros163
Zeros (%)92.6%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T01:48:13.223315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum419
Range419
Interquartile range (IQR)0

Descriptive statistics

Standard deviation33.295916
Coefficient of variation (CV)8.5423926
Kurtosis140.44169
Mean3.8977273
Median Absolute Deviation (MAD)0
Skewness11.445496
Sum686
Variance1108.6181
MonotonicityNot monotonic
2023-12-13T01:48:13.334376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 163
92.6%
1 5
 
2.8%
3 2
 
1.1%
67 1
 
0.6%
419 1
 
0.6%
5 1
 
0.6%
94 1
 
0.6%
88 1
 
0.6%
2 1
 
0.6%
ValueCountFrequency (%)
0 163
92.6%
1 5
 
2.8%
2 1
 
0.6%
3 2
 
1.1%
5 1
 
0.6%
67 1
 
0.6%
88 1
 
0.6%
94 1
 
0.6%
419 1
 
0.6%
ValueCountFrequency (%)
419 1
 
0.6%
94 1
 
0.6%
88 1
 
0.6%
67 1
 
0.6%
5 1
 
0.6%
3 2
 
1.1%
2 1
 
0.6%
1 5
 
2.8%
0 163
92.6%

성매매보호송치
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
175 
21
 
1

Length

Max length2
Median length1
Mean length1.0056818
Min length1

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
0 175
99.4%
21 1
 
0.6%

Length

2023-12-13T01:48:13.466953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:48:13.570474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 175
99.4%
21 1
 
0.6%

아동보호송치
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
174 
7
 
1
54
 
1

Length

Max length2
Median length1
Mean length1.0056818
Min length1

Unique

Unique2 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 174
98.9%
7 1
 
0.6%
54 1
 
0.6%

Length

2023-12-13T01:48:13.669448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:48:13.768371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.9%
7 1
 
0.6%
54 1
 
0.6%

기소유예
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.107955
Minimum0
Maximum2029
Zeros48
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T01:48:13.886378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q337
95-th percentile351.5
Maximum2029
Range2029
Interquartile range (IQR)37

Descriptive statistics

Standard deviation212.16615
Coefficient of variation (CV)3.0700684
Kurtosis48.316174
Mean69.107955
Median Absolute Deviation (MAD)6
Skewness6.2694587
Sum12163
Variance45014.474
MonotonicityNot monotonic
2023-12-13T01:48:14.043607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48
27.3%
1 17
 
9.7%
2 10
 
5.7%
6 9
 
5.1%
7 5
 
2.8%
3 5
 
2.8%
9 5
 
2.8%
37 4
 
2.3%
4 3
 
1.7%
12 3
 
1.7%
Other values (59) 67
38.1%
ValueCountFrequency (%)
0 48
27.3%
1 17
 
9.7%
2 10
 
5.7%
3 5
 
2.8%
4 3
 
1.7%
5 2
 
1.1%
6 9
 
5.1%
7 5
 
2.8%
8 2
 
1.1%
9 5
 
2.8%
ValueCountFrequency (%)
2029 1
0.6%
1259 1
0.6%
974 1
0.6%
555 1
0.6%
513 1
0.6%
470 1
0.6%
428 1
0.6%
372 1
0.6%
362 1
0.6%
348 1
0.6%

혐의없음
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.232955
Minimum0
Maximum3727
Zeros26
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T01:48:14.190779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median12
Q349
95-th percentile292.25
Maximum3727
Range3727
Interquartile range (IQR)47

Descriptive statistics

Standard deviation299.32734
Coefficient of variation (CV)3.9786731
Kurtosis128.24261
Mean75.232955
Median Absolute Deviation (MAD)11
Skewness10.654642
Sum13241
Variance89596.854
MonotonicityNot monotonic
2023-12-13T01:48:14.336136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
 
14.8%
2 17
 
9.7%
1 15
 
8.5%
5 6
 
3.4%
3 5
 
2.8%
7 5
 
2.8%
18 5
 
2.8%
12 5
 
2.8%
23 4
 
2.3%
13 4
 
2.3%
Other values (64) 84
47.7%
ValueCountFrequency (%)
0 26
14.8%
1 15
8.5%
2 17
9.7%
3 5
 
2.8%
4 3
 
1.7%
5 6
 
3.4%
6 2
 
1.1%
7 5
 
2.8%
8 2
 
1.1%
10 3
 
1.7%
ValueCountFrequency (%)
3727 1
0.6%
711 1
0.6%
634 1
0.6%
561 1
0.6%
497 1
0.6%
438 1
0.6%
418 1
0.6%
342 1
0.6%
326 1
0.6%
281 1
0.6%

죄가안됨
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8920455
Minimum0
Maximum166
Zeros154
Zeros (%)87.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T01:48:14.454153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6.5
Maximum166
Range166
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.137721
Coefficient of variation (CV)6.9436602
Kurtosis141.28428
Mean1.8920455
Median Absolute Deviation (MAD)0
Skewness11.434611
Sum333
Variance172.59971
MonotonicityNot monotonic
2023-12-13T01:48:14.555197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 154
87.5%
1 9
 
5.1%
31 2
 
1.1%
6 1
 
0.6%
5 1
 
0.6%
13 1
 
0.6%
166 1
 
0.6%
3 1
 
0.6%
10 1
 
0.6%
26 1
 
0.6%
Other values (4) 4
 
2.3%
ValueCountFrequency (%)
0 154
87.5%
1 9
 
5.1%
2 1
 
0.6%
3 1
 
0.6%
5 1
 
0.6%
6 1
 
0.6%
8 1
 
0.6%
10 1
 
0.6%
11 1
 
0.6%
12 1
 
0.6%
ValueCountFrequency (%)
166 1
0.6%
31 2
1.1%
26 1
0.6%
13 1
0.6%
12 1
0.6%
11 1
0.6%
10 1
0.6%
8 1
0.6%
6 1
0.6%
5 1
0.6%

공소권없음
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.176136
Minimum0
Maximum6558
Zeros82
Zeros (%)46.6%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T01:48:14.682395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34.25
95-th percentile142
Maximum6558
Range6558
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation607.83604
Coefficient of variation (CV)7.0534148
Kurtosis91.020625
Mean86.176136
Median Absolute Deviation (MAD)1
Skewness9.3681666
Sum15167
Variance369464.65
MonotonicityNot monotonic
2023-12-13T01:48:14.795877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 82
46.6%
1 23
 
13.1%
3 10
 
5.7%
5 10
 
5.7%
2 9
 
5.1%
4 8
 
4.5%
7 2
 
1.1%
27 2
 
1.1%
6 2
 
1.1%
31 2
 
1.1%
Other values (24) 26
 
14.8%
ValueCountFrequency (%)
0 82
46.6%
1 23
 
13.1%
2 9
 
5.1%
3 10
 
5.7%
4 8
 
4.5%
5 10
 
5.7%
6 2
 
1.1%
7 2
 
1.1%
8 2
 
1.1%
9 1
 
0.6%
ValueCountFrequency (%)
6558 1
0.6%
4604 1
0.6%
901 1
0.6%
637 1
0.6%
510 1
0.6%
311 1
0.6%
255 1
0.6%
180 1
0.6%
157 1
0.6%
137 1
0.6%

기소중지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.380682
Minimum0
Maximum2319
Zeros89
Zeros (%)50.6%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T01:48:14.901754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile77
Maximum2319
Range2319
Interquartile range (IQR)6

Descriptive statistics

Standard deviation176.76469
Coefficient of variation (CV)6.9645369
Kurtosis164.60489
Mean25.380682
Median Absolute Deviation (MAD)0
Skewness12.645818
Sum4467
Variance31245.757
MonotonicityNot monotonic
2023-12-13T01:48:15.017150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 89
50.6%
1 20
 
11.4%
3 8
 
4.5%
2 6
 
3.4%
4 5
 
2.8%
6 5
 
2.8%
11 4
 
2.3%
7 3
 
1.7%
5 3
 
1.7%
46 2
 
1.1%
Other values (28) 31
 
17.6%
ValueCountFrequency (%)
0 89
50.6%
1 20
 
11.4%
2 6
 
3.4%
3 8
 
4.5%
4 5
 
2.8%
5 3
 
1.7%
6 5
 
2.8%
7 3
 
1.7%
8 1
 
0.6%
10 1
 
0.6%
ValueCountFrequency (%)
2319 1
0.6%
272 1
0.6%
142 1
0.6%
131 1
0.6%
107 1
0.6%
100 1
0.6%
98 1
0.6%
83 1
0.6%
80 1
0.6%
76 1
0.6%

참고인중지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1931818
Minimum0
Maximum241
Zeros138
Zeros (%)78.4%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T01:48:15.116875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.25
Maximum241
Range241
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.334266
Coefficient of variation (CV)8.3596655
Kurtosis167.175
Mean2.1931818
Median Absolute Deviation (MAD)0
Skewness12.788129
Sum386
Variance336.14532
MonotonicityNot monotonic
2023-12-13T01:48:15.208828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 138
78.4%
1 16
 
9.1%
2 8
 
4.5%
3 3
 
1.7%
5 2
 
1.1%
18 2
 
1.1%
4 2
 
1.1%
241 1
 
0.6%
23 1
 
0.6%
10 1
 
0.6%
Other values (2) 2
 
1.1%
ValueCountFrequency (%)
0 138
78.4%
1 16
 
9.1%
2 8
 
4.5%
3 3
 
1.7%
4 2
 
1.1%
5 2
 
1.1%
6 1
 
0.6%
10 1
 
0.6%
11 1
 
0.6%
18 2
 
1.1%
ValueCountFrequency (%)
241 1
 
0.6%
23 1
 
0.6%
18 2
 
1.1%
11 1
 
0.6%
10 1
 
0.6%
6 1
 
0.6%
5 2
 
1.1%
4 2
 
1.1%
3 3
 
1.7%
2 8
4.5%

Interactions

2023-12-13T01:48:09.597042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:57.500374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:58.520654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:59.737532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:00.981425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:02.124111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:03.261423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:04.844802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:06.137468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:07.363477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:08.477765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:09.696008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:57.585513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:58.630600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:59.852183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:01.100173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:02.261020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:03.366118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:04.956133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:06.257532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:07.463391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:08.589808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:09.806903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:57.671256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:58.719899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:59.984742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:01.234621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:02.375988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:03.490043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:05.074015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:06.385827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:07.556426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:08.695566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:09.904054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:57.753158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:58.806727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:00.096680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:01.338641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:02.459286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:03.601145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:05.184408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:06.493154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:07.636809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:08.788264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:10.020285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:57.830413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:58.903077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:00.200608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:01.435381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:02.556911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:03.715102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:05.307072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:06.617952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:07.721092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:08.877222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:10.106552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:57.908605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:59.011569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:00.305869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:01.522779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:02.648399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:03.808230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:05.409642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:06.723111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:07.825033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:08.976197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:10.216842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:58.010406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:59.131197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:00.429744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:01.615274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:02.752974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:03.951506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:05.524203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:06.832013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:07.931762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:09.084754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:10.326099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:58.116166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:59.253573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:00.545841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:01.703697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:02.870019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:04.075218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:05.673527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:06.946371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:08.039066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:09.183558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:10.416186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:58.212266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:59.366208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:00.635784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:01.800745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:02.986920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:04.532121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:05.790491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:07.045550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:08.153424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:09.275154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:10.505426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:58.333010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:59.489274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:00.759617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:01.884853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:03.080780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:04.651285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:05.901620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:07.163337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:08.263804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:09.379833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:10.612309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:58.416290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:59.611492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:00.865212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:01.987022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:03.170174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:04.749140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:06.010358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:07.257210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:08.363195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:09.476862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:48:15.287034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구공판_구속구공판_불구속구약식소년보호송치가정보호송치성매매보호송치아동보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지
구공판_구속1.0000.9300.7320.9150.3310.0000.3440.8380.7600.3980.2530.7121.000
구공판_불구속0.9301.0000.8520.8710.5520.0000.5400.7970.8000.6050.5120.7481.000
구약식0.7320.8521.0000.9080.4510.0000.5400.6350.5340.5400.6350.5390.392
소년보호송치0.9150.8710.9081.0000.6670.0000.7120.8430.5720.7320.8380.3890.392
가정보호송치0.3310.5520.4510.6671.0000.0000.6710.8110.2670.7160.8900.0000.000
성매매보호송치0.0000.0000.0000.0000.0001.0000.0000.6480.0000.0000.0000.0000.000
아동보호송치0.3440.5400.5400.7120.6710.0001.0000.9370.5920.9410.6710.0000.000
기소유예0.8380.7970.6350.8430.8110.6480.9371.0000.9860.9480.7860.9371.000
혐의없음0.7600.8000.5340.5720.2670.0000.5920.9861.0000.6460.2670.9411.000
죄가안됨0.3980.6050.5400.7320.7160.0000.9410.9480.6461.0000.6710.0000.000
공소권없음0.2530.5120.6350.8380.8900.0000.6710.7860.2670.6711.0000.0000.000
기소중지0.7120.7480.5390.3890.0000.0000.0000.9370.9410.0000.0001.0001.000
참고인중지1.0001.0000.3920.3920.0000.0000.0001.0001.0000.0000.0001.0001.000
2023-12-13T01:48:15.415149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아동보호송치성매매보호송치
아동보호송치1.0000.000
성매매보호송치0.0001.000
2023-12-13T01:48:15.499873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구공판_구속구공판_불구속구약식소년보호송치가정보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지성매매보호송치아동보호송치
구공판_구속1.0000.6730.2270.4300.3420.3200.4460.3240.4120.4930.4110.0000.275
구공판_불구속0.6731.0000.5840.4740.3200.6030.6610.3900.5880.7130.4770.0000.478
구약식0.2270.5841.0000.4630.1900.8280.7680.3930.5930.6600.3960.0000.478
소년보호송치0.4300.4740.4631.0000.3570.5550.5190.4110.4170.4540.2870.0000.695
가정보호송치0.3420.3200.1900.3571.0000.2430.2680.3700.2630.1870.2090.0000.699
기소유예0.3200.6030.8280.5550.2431.0000.7840.4120.6220.6800.4160.4670.691
혐의없음0.4460.6610.7680.5190.2680.7841.0000.4460.7060.7440.4950.0000.265
죄가안됨0.3240.3900.3930.4110.3700.4120.4461.0000.4200.3480.1780.0000.703
공소권없음0.4120.5880.5930.4170.2630.6220.7060.4201.0000.7120.4080.0000.699
기소중지0.4930.7130.6600.4540.1870.6800.7440.3480.7121.0000.5400.0000.000
참고인중지0.4110.4770.3960.2870.2090.4160.4950.1780.4080.5401.0000.0000.000
성매매보호송치0.0000.0000.0000.0000.0000.4670.0000.0000.0000.0000.0001.0000.000
아동보호송치0.2750.4780.4780.6950.6990.6910.2650.7030.6990.0000.0000.0001.000

Missing values

2023-12-13T01:48:10.764663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:48:10.970267image/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
0절도1612679552400002029634648553
1불법사용0024000820000
2침입절도29411300016120710
3장물4917300024270131
4사기32113521038640001259372713112319241
5컴퓨터등사용사기3141310003761001422
6부당이득0000000020000
7편의시설부정이용00500009120301
8전기통신금융사기피해금환급에관한특별법49110000515902830
9보험사기방지특별법11131300037150010
범죄분류구공판_구속구공판_불구속구약식소년보호송치가정보호송치성매매보호송치아동보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지
166특허법03000000110500
167폐기물관리법0100000010000
168풍속영업의 규제에 관한법률00160000120010
169하천법0010000010000
170학교보건법0100000000000
171학원의 설립운영 및 과외교습에 관한법률00130000660000
172화물자동차운수사업법0088000052350021
173화재예방,소방시설설치유지 및 안전관리에 관한법률0010000000000
174화학물질관리법7415000110000
175기타특별법23195102531003724181277311