Overview

Dataset statistics

Number of variables14
Number of observations179
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.0 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 청주지방검찰청의 범죄자 처분결과에 관한 통계임.
Author대검찰청
URLhttps://www.data.go.kr/data/15084992/fileData.do

Alerts

구공판_구속 is highly overall correlated with 구공판_불구속High correlation
구공판_불구속 is highly overall correlated with 구공판_구속 and 6 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 3 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
아동보호송치 is highly overall correlated with 가정보호송치 and 3 other fieldsHigh correlation
성매매보호송치 is highly imbalanced (95.0%)Imbalance
아동보호송치 is highly imbalanced (89.8%)Imbalance
범죄분류 has unique valuesUnique
구공판_구속 has 100 (55.9%) zerosZeros
구공판_불구속 has 57 (31.8%) zerosZeros
구약식 has 37 (20.7%) zerosZeros
소년보호송치 has 140 (78.2%) zerosZeros
가정보호송치 has 170 (95.0%) zerosZeros
기소유예 has 40 (22.3%) zerosZeros
혐의없음 has 31 (17.3%) zerosZeros
죄가안됨 has 163 (91.1%) zerosZeros
공소권없음 has 90 (50.3%) zerosZeros
기소중지 has 102 (57.0%) zerosZeros
참고인중지 has 153 (85.5%) zerosZeros

Reproduction

Analysis started2023-12-12 06:37:09.845794
Analysis finished2023-12-12 06:37:24.006627
Duration14.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct179
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T15:37:24.172728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length22
Mean length8.0837989
Min length2

Characters and Unicode

Total characters1447
Distinct characters235
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

Unique179 ?
Unique (%)100.0%

Sample

1st row절도
2nd row불법사용
3rd row침입절도
4th row장물
5th row사기
ValueCountFrequency (%)
관한법률 22
 
7.6%
19
 
6.6%
관리에 3
 
1.0%
마약류관리에 3
 
1.0%
아동·청소년의 2
 
0.7%
사업법 2
 
0.7%
성보호에 2
 
0.7%
이용에 2
 
0.7%
보장법 2
 
0.7%
알선등 1
 
0.3%
Other values (230) 230
79.9%
2023-12-12T15:37:24.535162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
109
 
7.5%
105
 
7.3%
57
 
3.9%
33
 
2.3%
32
 
2.2%
28
 
1.9%
27
 
1.9%
24
 
1.7%
24
 
1.7%
23
 
1.6%
Other values (225) 985
68.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1291
89.2%
Space Separator 109
 
7.5%
Other Punctuation 17
 
1.2%
Open Punctuation 15
 
1.0%
Close Punctuation 15
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
105
 
8.1%
57
 
4.4%
33
 
2.6%
32
 
2.5%
28
 
2.2%
27
 
2.1%
24
 
1.9%
24
 
1.9%
23
 
1.8%
22
 
1.7%
Other values (219) 916
71.0%
Other Punctuation
ValueCountFrequency (%)
, 12
70.6%
· 4
 
23.5%
/ 1
 
5.9%
Space Separator
ValueCountFrequency (%)
109
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1291
89.2%
Common 156
 
10.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
105
 
8.1%
57
 
4.4%
33
 
2.6%
32
 
2.5%
28
 
2.2%
27
 
2.1%
24
 
1.9%
24
 
1.9%
23
 
1.8%
22
 
1.7%
Other values (219) 916
71.0%
Common
ValueCountFrequency (%)
109
69.9%
( 15
 
9.6%
) 15
 
9.6%
, 12
 
7.7%
· 4
 
2.6%
/ 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1291
89.2%
ASCII 152
 
10.5%
None 4
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
109
71.7%
( 15
 
9.9%
) 15
 
9.9%
, 12
 
7.9%
/ 1
 
0.7%
Hangul
ValueCountFrequency (%)
105
 
8.1%
57
 
4.4%
33
 
2.6%
32
 
2.5%
28
 
2.2%
27
 
2.1%
24
 
1.9%
24
 
1.9%
23
 
1.8%
22
 
1.7%
Other values (219) 916
71.0%
None
ValueCountFrequency (%)
· 4
100.0%

구공판_구속
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.698324
Minimum0
Maximum178
Zeros100
Zeros (%)55.9%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T15:37:25.030494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile18.2
Maximum178
Range178
Interquartile range (IQR)2

Descriptive statistics

Standard deviation17.544119
Coefficient of variation (CV)3.7341229
Kurtosis65.385396
Mean4.698324
Median Absolute Deviation (MAD)0
Skewness7.5353693
Sum841
Variance307.79612
MonotonicityNot monotonic
2023-12-12T15:37:25.178121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 100
55.9%
1 27
 
15.1%
2 14
 
7.8%
4 6
 
3.4%
9 3
 
1.7%
14 3
 
1.7%
3 3
 
1.7%
13 2
 
1.1%
30 2
 
1.1%
5 2
 
1.1%
Other values (14) 17
 
9.5%
ValueCountFrequency (%)
0 100
55.9%
1 27
 
15.1%
2 14
 
7.8%
3 3
 
1.7%
4 6
 
3.4%
5 2
 
1.1%
7 2
 
1.1%
8 2
 
1.1%
9 3
 
1.7%
10 2
 
1.1%
ValueCountFrequency (%)
178 1
0.6%
124 1
0.6%
57 1
0.6%
40 1
0.6%
37 1
0.6%
30 2
1.1%
22 1
0.6%
20 1
0.6%
18 1
0.6%
16 1
0.6%

구공판_불구속
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.932961
Minimum0
Maximum798
Zeros57
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T15:37:25.304628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q313
95-th percentile119.5
Maximum798
Range798
Interquartile range (IQR)13

Descriptive statistics

Standard deviation85.947959
Coefficient of variation (CV)3.1911812
Kurtosis43.458883
Mean26.932961
Median Absolute Deviation (MAD)2
Skewness6.0014137
Sum4821
Variance7387.0517
MonotonicityNot monotonic
2023-12-12T15:37:25.435115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 57
31.8%
1 25
14.0%
2 14
 
7.8%
4 7
 
3.9%
3 5
 
2.8%
5 5
 
2.8%
6 5
 
2.8%
7 4
 
2.2%
18 4
 
2.2%
33 3
 
1.7%
Other values (39) 50
27.9%
ValueCountFrequency (%)
0 57
31.8%
1 25
14.0%
2 14
 
7.8%
3 5
 
2.8%
4 7
 
3.9%
5 5
 
2.8%
6 5
 
2.8%
7 4
 
2.2%
8 3
 
1.7%
9 2
 
1.1%
ValueCountFrequency (%)
798 1
0.6%
510 1
0.6%
377 1
0.6%
314 1
0.6%
255 1
0.6%
247 1
0.6%
220 1
0.6%
157 1
0.6%
133 1
0.6%
118 1
0.6%

구약식
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct76
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.2514
Minimum0
Maximum5767
Zeros37
Zeros (%)20.7%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T15:37:25.581443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q339.5
95-th percentile372.8
Maximum5767
Range5767
Interquartile range (IQR)38.5

Descriptive statistics

Standard deviation472.78543
Coefficient of variation (CV)4.5789737
Kurtosis117.97873
Mean103.2514
Median Absolute Deviation (MAD)8
Skewness10.183155
Sum18482
Variance223526.07
MonotonicityNot monotonic
2023-12-12T15:37:25.710733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
20.7%
3 14
 
7.8%
1 11
 
6.1%
2 11
 
6.1%
5 7
 
3.9%
10 7
 
3.9%
8 5
 
2.8%
21 4
 
2.2%
26 3
 
1.7%
13 3
 
1.7%
Other values (66) 77
43.0%
ValueCountFrequency (%)
0 37
20.7%
1 11
 
6.1%
2 11
 
6.1%
3 14
 
7.8%
4 2
 
1.1%
5 7
 
3.9%
6 3
 
1.7%
7 1
 
0.6%
8 5
 
2.8%
9 2
 
1.1%
ValueCountFrequency (%)
5767 1
0.6%
1883 1
0.6%
1071 1
0.6%
754 1
0.6%
753 1
0.6%
711 1
0.6%
707 1
0.6%
699 1
0.6%
497 1
0.6%
359 1
0.6%

소년보호송치
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1117318
Minimum0
Maximum216
Zeros140
Zeros (%)78.2%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T15:37:25.860192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile16.2
Maximum216
Range216
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.622529
Coefficient of variation (CV)4.7723269
Kurtosis79.970012
Mean4.1117318
Median Absolute Deviation (MAD)0
Skewness8.2226368
Sum736
Variance385.04363
MonotonicityNot monotonic
2023-12-12T15:37:26.052387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 140
78.2%
1 8
 
4.5%
6 4
 
2.2%
3 4
 
2.2%
9 3
 
1.7%
2 3
 
1.7%
5 2
 
1.1%
4 2
 
1.1%
216 1
 
0.6%
21 1
 
0.6%
Other values (11) 11
 
6.1%
ValueCountFrequency (%)
0 140
78.2%
1 8
 
4.5%
2 3
 
1.7%
3 4
 
2.2%
4 2
 
1.1%
5 2
 
1.1%
6 4
 
2.2%
7 1
 
0.6%
9 3
 
1.7%
11 1
 
0.6%
ValueCountFrequency (%)
216 1
0.6%
83 1
0.6%
81 1
0.6%
67 1
0.6%
64 1
0.6%
23 1
0.6%
22 1
0.6%
21 1
0.6%
18 1
0.6%
16 1
0.6%

가정보호송치
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1620112
Minimum0
Maximum360
Zeros170
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T15:37:26.354624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.1
Maximum360
Range360
Interquartile range (IQR)0

Descriptive statistics

Standard deviation28.416278
Coefficient of variation (CV)8.9867734
Kurtosis142.57637
Mean3.1620112
Median Absolute Deviation (MAD)0
Skewness11.573959
Sum566
Variance807.48484
MonotonicityNot monotonic
2023-12-12T15:37:26.494981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 170
95.0%
1 4
 
2.2%
49 1
 
0.6%
360 1
 
0.6%
108 1
 
0.6%
43 1
 
0.6%
2 1
 
0.6%
ValueCountFrequency (%)
0 170
95.0%
1 4
 
2.2%
2 1
 
0.6%
43 1
 
0.6%
49 1
 
0.6%
108 1
 
0.6%
360 1
 
0.6%
ValueCountFrequency (%)
360 1
 
0.6%
108 1
 
0.6%
49 1
 
0.6%
43 1
 
0.6%
2 1
 
0.6%
1 4
 
2.2%
0 170
95.0%

성매매보호송치
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
178 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
0 178
99.4%
3 1
 
0.6%

Length

2023-12-12T15:37:26.625091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:37:26.715066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 178
99.4%
3 1
 
0.6%

아동보호송치
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
174 
1
 
2
7
 
1
2
 
1
84
 
1

Length

Max length2
Median length1
Mean length1.0055866
Min length1

Unique

Unique3 ?
Unique (%)1.7%

Sample

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

Common Values

ValueCountFrequency (%)
0 174
97.2%
1 2
 
1.1%
7 1
 
0.6%
2 1
 
0.6%
84 1
 
0.6%

Length

2023-12-12T15:37:26.825641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:37:26.955036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 174
97.2%
1 2
 
1.1%
7 1
 
0.6%
2 1
 
0.6%
84 1
 
0.6%

기소유예
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.625698
Minimum0
Maximum1443
Zeros40
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T15:37:27.079305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q326
95-th percentile261.5
Maximum1443
Range1443
Interquartile range (IQR)25

Descriptive statistics

Standard deviation166.53181
Coefficient of variation (CV)3.0485982
Kurtosis35.812098
Mean54.625698
Median Absolute Deviation (MAD)4
Skewness5.5031754
Sum9778
Variance27732.842
MonotonicityNot monotonic
2023-12-12T15:37:27.234478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 40
22.3%
1 20
 
11.2%
2 14
 
7.8%
4 8
 
4.5%
3 8
 
4.5%
6 7
 
3.9%
9 6
 
3.4%
18 4
 
2.2%
10 4
 
2.2%
7 3
 
1.7%
Other values (54) 65
36.3%
ValueCountFrequency (%)
0 40
22.3%
1 20
11.2%
2 14
 
7.8%
3 8
 
4.5%
4 8
 
4.5%
5 3
 
1.7%
6 7
 
3.9%
7 3
 
1.7%
8 1
 
0.6%
9 6
 
3.4%
ValueCountFrequency (%)
1443 1
0.6%
1019 1
0.6%
809 1
0.6%
651 1
0.6%
566 1
0.6%
463 1
0.6%
323 1
0.6%
302 1
0.6%
266 1
0.6%
261 1
0.6%

혐의없음
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.938547
Minimum0
Maximum2297
Zeros31
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T15:37:27.431369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q324
95-th percentile229.1
Maximum2297
Range2297
Interquartile range (IQR)23

Descriptive statistics

Standard deviation188.03595
Coefficient of variation (CV)3.8422871
Kurtosis116.10176
Mean48.938547
Median Absolute Deviation (MAD)5
Skewness9.943139
Sum8760
Variance35357.519
MonotonicityNot monotonic
2023-12-12T15:37:27.582516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31
17.3%
1 23
 
12.8%
2 19
 
10.6%
7 10
 
5.6%
4 8
 
4.5%
5 7
 
3.9%
3 5
 
2.8%
24 4
 
2.2%
8 4
 
2.2%
9 4
 
2.2%
Other values (46) 64
35.8%
ValueCountFrequency (%)
0 31
17.3%
1 23
12.8%
2 19
10.6%
3 5
 
2.8%
4 8
 
4.5%
5 7
 
3.9%
6 4
 
2.2%
7 10
 
5.6%
8 4
 
2.2%
9 4
 
2.2%
ValueCountFrequency (%)
2297 1
0.6%
470 1
0.6%
437 1
0.6%
392 1
0.6%
339 1
0.6%
326 1
0.6%
312 1
0.6%
276 1
0.6%
257 1
0.6%
226 1
0.6%

죄가안됨
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36312849
Minimum0
Maximum16
Zeros163
Zeros (%)91.1%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T15:37:27.727729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7759262
Coefficient of variation (CV)4.8906274
Kurtosis50.038442
Mean0.36312849
Median Absolute Deviation (MAD)0
Skewness6.752334
Sum65
Variance3.1539138
MonotonicityNot monotonic
2023-12-12T15:37:27.867081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 163
91.1%
1 6
 
3.4%
2 4
 
2.2%
7 1
 
0.6%
13 1
 
0.6%
8 1
 
0.6%
16 1
 
0.6%
3 1
 
0.6%
4 1
 
0.6%
ValueCountFrequency (%)
0 163
91.1%
1 6
 
3.4%
2 4
 
2.2%
3 1
 
0.6%
4 1
 
0.6%
7 1
 
0.6%
8 1
 
0.6%
13 1
 
0.6%
16 1
 
0.6%
ValueCountFrequency (%)
16 1
 
0.6%
13 1
 
0.6%
8 1
 
0.6%
7 1
 
0.6%
4 1
 
0.6%
3 1
 
0.6%
2 4
 
2.2%
1 6
 
3.4%
0 163
91.1%

공소권없음
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.96648
Minimum0
Maximum5266
Zeros90
Zeros (%)50.3%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T15:37:28.036334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile92.4
Maximum5266
Range5266
Interquartile range (IQR)4

Descriptive statistics

Standard deviation464.3808
Coefficient of variation (CV)7.7440063
Kurtosis102.62014
Mean59.96648
Median Absolute Deviation (MAD)0
Skewness9.9481087
Sum10734
Variance215649.53
MonotonicityNot monotonic
2023-12-12T15:37:28.158320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 90
50.3%
1 25
 
14.0%
2 13
 
7.3%
4 6
 
3.4%
3 5
 
2.8%
11 5
 
2.8%
5 4
 
2.2%
6 3
 
1.7%
30 2
 
1.1%
8 2
 
1.1%
Other values (23) 24
 
13.4%
ValueCountFrequency (%)
0 90
50.3%
1 25
 
14.0%
2 13
 
7.3%
3 5
 
2.8%
4 6
 
3.4%
5 4
 
2.2%
6 3
 
1.7%
7 2
 
1.1%
8 2
 
1.1%
10 1
 
0.6%
ValueCountFrequency (%)
5266 1
0.6%
3307 1
0.6%
372 1
0.6%
301 1
0.6%
217 1
0.6%
114 1
0.6%
113 1
0.6%
107 1
0.6%
105 1
0.6%
91 1
0.6%

기소중지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.921788
Minimum0
Maximum1296
Zeros102
Zeros (%)57.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T15:37:28.276294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile32.2
Maximum1296
Range1296
Interquartile range (IQR)3

Descriptive statistics

Standard deviation99.97137
Coefficient of variation (CV)6.6996912
Kurtosis153.88486
Mean14.921788
Median Absolute Deviation (MAD)0
Skewness12.068128
Sum2671
Variance9994.2747
MonotonicityNot monotonic
2023-12-12T15:37:28.405751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 102
57.0%
1 26
 
14.5%
4 8
 
4.5%
3 6
 
3.4%
2 4
 
2.2%
18 4
 
2.2%
32 2
 
1.1%
11 2
 
1.1%
29 2
 
1.1%
5 2
 
1.1%
Other values (21) 21
 
11.7%
ValueCountFrequency (%)
0 102
57.0%
1 26
 
14.5%
2 4
 
2.2%
3 6
 
3.4%
4 8
 
4.5%
5 2
 
1.1%
6 1
 
0.6%
9 1
 
0.6%
11 2
 
1.1%
14 1
 
0.6%
ValueCountFrequency (%)
1296 1
0.6%
259 1
0.6%
177 1
0.6%
111 1
0.6%
80 1
0.6%
74 1
0.6%
57 1
0.6%
39 1
0.6%
34 1
0.6%
32 2
1.1%

참고인중지
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8603352
Minimum0
Maximum63
Zeros153
Zeros (%)85.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T15:37:28.537876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum63
Range63
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.0544831
Coefficient of variation (CV)5.8750161
Kurtosis130.31378
Mean0.8603352
Median Absolute Deviation (MAD)0
Skewness10.818005
Sum154
Variance25.5478
MonotonicityNot monotonic
2023-12-12T15:37:28.653208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 153
85.5%
1 13
 
7.3%
4 3
 
1.7%
3 2
 
1.1%
63 1
 
0.6%
8 1
 
0.6%
12 1
 
0.6%
11 1
 
0.6%
7 1
 
0.6%
5 1
 
0.6%
Other values (2) 2
 
1.1%
ValueCountFrequency (%)
0 153
85.5%
1 13
 
7.3%
2 1
 
0.6%
3 2
 
1.1%
4 3
 
1.7%
5 1
 
0.6%
7 1
 
0.6%
8 1
 
0.6%
11 1
 
0.6%
12 1
 
0.6%
ValueCountFrequency (%)
63 1
 
0.6%
15 1
 
0.6%
12 1
 
0.6%
11 1
 
0.6%
8 1
 
0.6%
7 1
 
0.6%
5 1
 
0.6%
4 3
1.7%
3 2
1.1%
2 1
 
0.6%

Interactions

2023-12-12T15:37:22.636448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:10.544520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:11.854095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:13.340113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:14.307876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:15.341498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.504727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:17.751101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:19.176077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:20.422986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:21.536386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:22.718342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:10.665049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:11.954116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:13.430126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:14.397749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:15.467991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.602123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:17.837718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:19.298722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:20.551216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:21.648679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:22.806262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:10.795854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:12.373260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:13.549251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:14.484921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:15.583763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.713207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:17.940511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:19.438430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:20.660519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:21.770111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:22.904257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:10.911075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:12.475362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:13.623240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:14.577453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:15.691648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.815569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:18.031868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:19.548880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:20.749662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:21.888087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:22.996641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:11.024609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:12.586752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:13.693279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:14.669054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:15.789401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.916945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:18.111700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:19.663575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:20.845588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:22.012218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:23.101087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:11.153389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:12.736701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:13.777494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:14.757181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:15.910483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:17.034621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:18.207353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:19.778813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:20.932740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:22.128485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:23.179221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:11.267639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:12.865590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:13.861136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:14.845151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.025228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:17.165745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:18.595088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:19.887920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:21.017542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:22.221964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:23.256234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:11.355131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:12.955305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:13.938152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:14.930548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.120393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:17.261827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:18.699880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:19.972607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:21.108051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:22.315242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:23.357742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:11.484612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:13.054947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:14.030716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:15.018630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.223933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:17.384559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:18.820490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:20.082897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:21.227760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:22.406990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:23.451553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:11.594825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:13.151690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:14.115594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:15.112347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.316999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:17.503943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:18.921802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:20.187837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:21.320763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:22.480992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:23.567106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:11.703781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:13.245531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:14.209748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:15.228077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.409160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:17.632608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:19.045716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:20.305176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:21.429312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:22.552203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:37:28.769400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구공판_구속구공판_불구속구약식소년보호송치가정보호송치성매매보호송치아동보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지
구공판_구속1.0000.8450.5210.8340.6280.0000.5550.8670.7670.5050.5480.9370.767
구공판_불구속0.8451.0000.9270.8930.5000.3410.4830.8570.7730.7250.8000.7600.747
구약식0.5210.9271.0000.6760.6520.0000.3560.8010.6940.5000.7170.3350.647
소년보호송치0.8340.8930.6761.0000.5230.0000.7610.8810.6980.5240.6620.7120.716
가정보호송치0.6280.5000.6520.5231.0000.0000.8380.9650.4180.9290.6710.0000.751
성매매보호송치0.0000.3410.0000.0000.0001.0000.0000.3210.0000.0000.0000.0000.000
아동보호송치0.5550.4830.3560.7610.8380.0001.0000.7510.1910.8320.7120.0000.450
기소유예0.8670.8570.8010.8810.9650.3210.7511.0000.9910.8230.6430.7890.933
혐의없음0.7670.7730.6940.6980.4180.0000.1910.9911.0000.3510.3630.6900.932
죄가안됨0.5050.7250.5000.5240.9290.0000.8320.8230.3511.0000.7600.0000.502
공소권없음0.5480.8000.7170.6620.6710.0000.7120.6430.3630.7601.0000.0000.340
기소중지0.9370.7600.3350.7120.0000.0000.0000.7890.6900.0000.0001.0000.672
참고인중지0.7670.7470.6470.7160.7510.0000.4500.9330.9320.5020.3400.6721.000
2023-12-12T15:37:28.921766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아동보호송치성매매보호송치
아동보호송치1.0000.000
성매매보호송치0.0001.000
2023-12-12T15:37:29.013115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구공판_구속구공판_불구속구약식소년보호송치가정보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지성매매보호송치아동보호송치
구공판_구속1.0000.6340.2990.4410.1890.3440.3870.2910.3630.4510.3100.0000.416
구공판_불구속0.6341.0000.5710.5330.1510.5340.6410.2940.5830.6440.4450.3590.333
구약식0.2990.5711.0000.4420.1650.8350.7260.2850.5920.5410.3960.0000.296
소년보호송치0.4410.5330.4421.0000.2090.5360.5130.3710.4800.5220.3990.0000.383
가정보호송치0.1890.1510.1650.2091.0000.2090.1760.4770.1570.1290.0730.0000.810
기소유예0.3440.5340.8350.5360.2091.0000.6610.3010.6160.6320.3780.2360.586
혐의없음0.3870.6410.7260.5130.1760.6611.0000.3420.5960.6300.4860.0000.156
죄가안됨0.2910.2940.2850.3710.4770.3010.3421.0000.2910.3120.1700.0000.636
공소권없음0.3630.5830.5920.4800.1570.6160.5960.2911.0000.5910.3680.0000.695
기소중지0.4510.6440.5410.5220.1290.6320.6300.3120.5911.0000.4980.0000.000
참고인중지0.3100.4450.3960.3990.0730.3780.4860.1700.3680.4981.0000.0000.381
성매매보호송치0.0000.3590.0000.0000.0000.2360.0000.0000.0000.0000.0001.0000.000
아동보호송치0.4160.3330.2960.3830.8100.5860.1560.6360.6950.0000.3810.0001.000

Missing values

2023-12-12T15:37:23.730191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:37:23.917975image/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
0절도1242204972160001443437048393
1불법사용112220001860000
2침입절도1433570003670340
3장물0431000650000
4사기17879870764000101922970105129663
5컴퓨터등사용사기036000039903250
6부당이득0000000100000
7편의시설부정이용0020000170000
8전기통신금융사기피해금환급에관한특별법0540000352401800
9보험사기방지특별법01360000590040
범죄분류구공판_구속구공판_불구속구약식소년보호송치가정보호송치성매매보호송치아동보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지
169출입국관리법01247000015140541
170통신비밀보호법0200000200000
171특가법(도주차량)4722600007360730
172특허법0010000020000
173폐기물관리법4153000003080110
174하천법0540000130200
175학원의 설립운영 및 과외교습에 관한법률0050000400000
176화물자동차운수사업법01100000620110
177화학물질관리법1000000010000
178기타특별법112477110000302221428182