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 서울중앙지방검찰청의 범죄자 처분결과에 관한 통계임.
Author대검찰청
URLhttps://www.data.go.kr/data/15084898/fileData.do

Alerts

구공판_구속 is highly overall correlated with 구공판_불구속 and 3 other fieldsHigh correlation
구공판_불구속 is highly overall correlated with 구공판_구속 and 8 other fieldsHigh correlation
구약식 is highly overall correlated with 구공판_불구속 and 6 other fieldsHigh correlation
소년보호송치 is highly overall correlated with 구공판_불구속 and 5 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 6 other fieldsHigh correlation
죄가안됨 is highly overall correlated with 아동보호송치High correlation
공소권없음 is highly overall correlated with 구공판_불구속 and 4 other fieldsHigh correlation
기소중지 is highly overall correlated with 구공판_구속 and 7 other fieldsHigh correlation
참고인중지 is highly overall correlated with 구공판_구속 and 3 other fieldsHigh correlation
성매매보호송치 is highly overall correlated with 구공판_구속 and 2 other fieldsHigh correlation
아동보호송치 is highly overall correlated with 구약식 and 3 other fieldsHigh correlation
성매매보호송치 is highly imbalanced (95.2%)Imbalance
아동보호송치 is highly imbalanced (86.2%)Imbalance
범죄분류 has unique valuesUnique
구공판_구속 has 75 (40.1%) zerosZeros
구공판_불구속 has 39 (20.9%) zerosZeros
구약식 has 29 (15.5%) zerosZeros
소년보호송치 has 138 (73.8%) zerosZeros
가정보호송치 has 178 (95.2%) zerosZeros
기소유예 has 35 (18.7%) zerosZeros
혐의없음 has 17 (9.1%) zerosZeros
죄가안됨 has 156 (83.4%) zerosZeros
공소권없음 has 67 (35.8%) zerosZeros
기소중지 has 71 (38.0%) zerosZeros
참고인중지 has 130 (69.5%) zerosZeros

Reproduction

Analysis started2023-12-12 22:20:12.739032
Analysis finished2023-12-12 22:20:24.874606
Duration12.14 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
2023-12-13T07:20:25.054031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length22
Mean length8.026738
Min length2

Characters and Unicode

Total characters1501
Distinct characters238
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

Unique187 ?
Unique (%)100.0%

Sample

1st row절도
2nd row불법사용
3rd row침입절도
4th row장물
5th row사기
ValueCountFrequency (%)
관한법률 25
 
8.4%
및 19
 
6.4%
관리에 4
 
1.3%
마약류관리에 3
 
1.0%
보호에 3
 
1.0%
운수사업법 2
 
0.7%
아동·청소년의 2
 
0.7%
처벌등에 2
 
0.7%
이용에 2
 
0.7%
성보호에 2
 
0.7%
Other values (233) 235
78.6%
2023-12-13T07:20:25.514855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
112
 
7.5%
법 106
 
7.1%
관 57
 
3.8%
한 35
 
2.3%
에 34
 
2.3%
률 30
 
2.0%
보 25
 
1.7%
의 25
 
1.7%
등 24
 
1.6%
리 24
 
1.6%
Other values (228) 1029
68.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1340
89.3%
Space Separator 112
 
7.5%
Other Punctuation 19
 
1.3%
Close Punctuation 15
 
1.0%
Open Punctuation 15
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
법 106
 
7.9%
관 57
 
4.3%
한 35
 
2.6%
에 34
 
2.5%
률 30
 
2.2%
보 25
 
1.9%
의 25
 
1.9%
등 24
 
1.8%
리 24
 
1.8%
사 22
 
1.6%
Other values (222) 958
71.5%
Other Punctuation
ValueCountFrequency (%)
, 12
63.2%
· 4
 
21.1%
/ 3
 
15.8%
Space Separator
ValueCountFrequency (%)
112
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1340
89.3%
Common 161
 
10.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
법 106
 
7.9%
관 57
 
4.3%
한 35
 
2.6%
에 34
 
2.5%
률 30
 
2.2%
보 25
 
1.9%
의 25
 
1.9%
등 24
 
1.8%
리 24
 
1.8%
사 22
 
1.6%
Other values (222) 958
71.5%
Common
ValueCountFrequency (%)
112
69.6%
) 15
 
9.3%
( 15
 
9.3%
, 12
 
7.5%
· 4
 
2.5%
/ 3
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1340
89.3%
ASCII 157
 
10.5%
None 4
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
112
71.3%
) 15
 
9.6%
( 15
 
9.6%
, 12
 
7.6%
/ 3
 
1.9%
Hangul
ValueCountFrequency (%)
법 106
 
7.9%
관 57
 
4.3%
한 35
 
2.6%
에 34
 
2.5%
률 30
 
2.2%
보 25
 
1.9%
의 25
 
1.9%
등 24
 
1.8%
리 24
 
1.8%
사 22
 
1.6%
Other values (222) 958
71.5%
None
ValueCountFrequency (%)
· 4
100.0%

구공판_구속
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.44385
Minimum0
Maximum567
Zeros75
Zeros (%)40.1%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T07:20:25.688426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37.5
95-th percentile54.1
Maximum567
Range567
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation52.483697
Coefficient of variation (CV)3.6336362
Kurtosis71.315601
Mean14.44385
Median Absolute Deviation (MAD)1
Skewness7.7289066
Sum2701
Variance2754.5385
MonotonicityNot monotonic
2023-12-13T07:20:26.134734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 75
40.1%
1 21
 
11.2%
4 14
 
7.5%
3 11
 
5.9%
2 10
 
5.3%
7 4
 
2.1%
9 4
 
2.1%
10 4
 
2.1%
6 4
 
2.1%
8 2
 
1.1%
Other values (31) 38
20.3%
ValueCountFrequency (%)
0 75
40.1%
1 21
 
11.2%
2 10
 
5.3%
3 11
 
5.9%
4 14
 
7.5%
5 1
 
0.5%
6 4
 
2.1%
7 4
 
2.1%
8 2
 
1.1%
9 4
 
2.1%
ValueCountFrequency (%)
567 1
0.5%
260 1
0.5%
246 1
0.5%
221 1
0.5%
80 1
0.5%
73 2
1.1%
65 1
0.5%
58 1
0.5%
55 1
0.5%
52 1
0.5%

구공판_불구속
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.898396
Minimum0
Maximum2685
Zeros39
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T07:20:26.310536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q336
95-th percentile314.3
Maximum2685
Range2685
Interquartile range (IQR)35

Descriptive statistics

Standard deviation217.40697
Coefficient of variation (CV)3.6295959
Kurtosis115.48968
Mean59.898396
Median Absolute Deviation (MAD)7
Skewness9.835146
Sum11201
Variance47265.791
MonotonicityNot monotonic
2023-12-13T07:20:26.479388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 39
20.9%
2 15
 
8.0%
1 15
 
8.0%
3 9
 
4.8%
5 7
 
3.7%
8 7
 
3.7%
23 4
 
2.1%
30 4
 
2.1%
7 4
 
2.1%
10 4
 
2.1%
Other values (61) 79
42.2%
ValueCountFrequency (%)
0 39
20.9%
1 15
 
8.0%
2 15
 
8.0%
3 9
 
4.8%
4 3
 
1.6%
5 7
 
3.7%
6 3
 
1.6%
7 4
 
2.1%
8 7
 
3.7%
9 1
 
0.5%
ValueCountFrequency (%)
2685 1
0.5%
544 1
0.5%
508 1
0.5%
446 2
1.1%
419 1
0.5%
402 1
0.5%
390 1
0.5%
377 1
0.5%
335 1
0.5%
266 1
0.5%

구약식
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct99
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.46524
Minimum0
Maximum4431
Zeros29
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T07:20:26.621091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median17
Q3127.5
95-th percentile840.7
Maximum4431
Range4431
Interquartile range (IQR)124.5

Descriptive statistics

Standard deviation489.29461
Coefficient of variation (CV)2.652503
Kurtosis36.746532
Mean184.46524
Median Absolute Deviation (MAD)17
Skewness5.3644097
Sum34495
Variance239409.22
MonotonicityNot monotonic
2023-12-13T07:20:26.774376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
15.5%
1 11
 
5.9%
3 10
 
5.3%
7 8
 
4.3%
2 6
 
3.2%
4 6
 
3.2%
6 6
 
3.2%
5 4
 
2.1%
15 4
 
2.1%
39 3
 
1.6%
Other values (89) 100
53.5%
ValueCountFrequency (%)
0 29
15.5%
1 11
 
5.9%
2 6
 
3.2%
3 10
 
5.3%
4 6
 
3.2%
5 4
 
2.1%
6 6
 
3.2%
7 8
 
4.3%
8 2
 
1.1%
10 1
 
0.5%
ValueCountFrequency (%)
4431 1
0.5%
2981 1
0.5%
1832 1
0.5%
1713 1
0.5%
1632 1
0.5%
1528 1
0.5%
1364 1
0.5%
1154 1
0.5%
910 1
0.5%
877 1
0.5%

소년보호송치
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5935829
Minimum0
Maximum247
Zeros138
Zeros (%)73.8%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T07:20:26.895364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile12.4
Maximum247
Range247
Interquartile range (IQR)1

Descriptive statistics

Standard deviation22.412208
Coefficient of variation (CV)4.8790254
Kurtosis79.775228
Mean4.5935829
Median Absolute Deviation (MAD)0
Skewness8.301722
Sum859
Variance502.30706
MonotonicityNot monotonic
2023-12-13T07:20:27.024351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 138
73.8%
1 9
 
4.8%
3 7
 
3.7%
2 5
 
2.7%
4 5
 
2.7%
5 4
 
2.1%
10 2
 
1.1%
11 2
 
1.1%
7 2
 
1.1%
6 2
 
1.1%
Other values (11) 11
 
5.9%
ValueCountFrequency (%)
0 138
73.8%
1 9
 
4.8%
2 5
 
2.7%
3 7
 
3.7%
4 5
 
2.7%
5 4
 
2.1%
6 2
 
1.1%
7 2
 
1.1%
9 1
 
0.5%
10 2
 
1.1%
ValueCountFrequency (%)
247 1
0.5%
128 1
0.5%
88 1
0.5%
71 1
0.5%
61 1
0.5%
29 1
0.5%
27 1
0.5%
24 1
0.5%
14 1
0.5%
13 1
0.5%

가정보호송치
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1283422
Minimum0
Maximum285
Zeros178
Zeros (%)95.2%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T07:20:27.122539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum285
Range285
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21.301335
Coefficient of variation (CV)10.008416
Kurtosis169.78397
Mean2.1283422
Median Absolute Deviation (MAD)0
Skewness12.806023
Sum398
Variance453.74688
MonotonicityNot monotonic
2023-12-13T07:20:27.214406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 178
95.2%
4 2
 
1.1%
1 2
 
1.1%
14 1
 
0.5%
285 1
 
0.5%
43 1
 
0.5%
44 1
 
0.5%
2 1
 
0.5%
ValueCountFrequency (%)
0 178
95.2%
1 2
 
1.1%
2 1
 
0.5%
4 2
 
1.1%
14 1
 
0.5%
43 1
 
0.5%
44 1
 
0.5%
285 1
 
0.5%
ValueCountFrequency (%)
285 1
 
0.5%
44 1
 
0.5%
43 1
 
0.5%
14 1
 
0.5%
4 2
 
1.1%
2 1
 
0.5%
1 2
 
1.1%
0 178
95.2%

성매매보호송치
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
186 
184
 
1

Length

Max length3
Median length1
Mean length1.0106952
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%
184 1
 
0.5%

Length

2023-12-13T07:20:27.335891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:20:27.440619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 186
99.5%
184 1
 
0.5%

아동보호송치
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
179 
1
 
5
3
 
1
2
 
1
89
 
1

Length

Max length2
Median length1
Mean length1.0053476
Min length1

Unique

Unique3 ?
Unique (%)1.6%

Sample

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

Common Values

ValueCountFrequency (%)
0 179
95.7%
1 5
 
2.7%
3 1
 
0.5%
2 1
 
0.5%
89 1
 
0.5%

Length

2023-12-13T07:20:27.534605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:20:27.646320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 179
95.7%
1 5
 
2.7%
3 1
 
0.5%
2 1
 
0.5%
89 1
 
0.5%

기소유예
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.75401
Minimum0
Maximum4256
Zeros35
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T07:20:27.744472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median10
Q355.5
95-th percentile415.9
Maximum4256
Range4256
Interquartile range (IQR)54.5

Descriptive statistics

Standard deviation387.4072
Coefficient of variation (CV)3.3181489
Kurtosis75.720568
Mean116.75401
Median Absolute Deviation (MAD)10
Skewness7.8842961
Sum21833
Variance150084.34
MonotonicityNot monotonic
2023-12-13T07:20:27.862276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35
 
18.7%
1 16
 
8.6%
3 10
 
5.3%
2 7
 
3.7%
14 6
 
3.2%
6 5
 
2.7%
8 5
 
2.7%
4 5
 
2.7%
7 4
 
2.1%
9 4
 
2.1%
Other values (75) 90
48.1%
ValueCountFrequency (%)
0 35
18.7%
1 16
8.6%
2 7
 
3.7%
3 10
 
5.3%
4 5
 
2.7%
5 2
 
1.1%
6 5
 
2.7%
7 4
 
2.1%
8 5
 
2.7%
9 4
 
2.1%
ValueCountFrequency (%)
4256 1
0.5%
2326 1
0.5%
1079 1
0.5%
951 1
0.5%
888 1
0.5%
756 1
0.5%
742 1
0.5%
578 1
0.5%
570 1
0.5%
421 1
0.5%

혐의없음
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct92
Distinct (%)49.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.97326
Minimum0
Maximum7876
Zeros17
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T07:20:27.982109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median17
Q376.5
95-th percentile582.1
Maximum7876
Range7876
Interquartile range (IQR)71.5

Descriptive statistics

Standard deviation602.42885
Coefficient of variation (CV)4.3038852
Kurtosis148.07885
Mean139.97326
Median Absolute Deviation (MAD)16
Skewness11.598104
Sum26175
Variance362920.52
MonotonicityNot monotonic
2023-12-13T07:20:28.150586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
9.1%
1 11
 
5.9%
5 9
 
4.8%
10 8
 
4.3%
7 7
 
3.7%
2 5
 
2.7%
13 5
 
2.7%
15 5
 
2.7%
6 5
 
2.7%
9 5
 
2.7%
Other values (82) 110
58.8%
ValueCountFrequency (%)
0 17
9.1%
1 11
5.9%
2 5
 
2.7%
3 3
 
1.6%
4 3
 
1.6%
5 9
4.8%
6 5
 
2.7%
7 7
3.7%
8 2
 
1.1%
9 5
 
2.7%
ValueCountFrequency (%)
7876 1
0.5%
1245 1
0.5%
1079 1
0.5%
864 1
0.5%
767 1
0.5%
755 1
0.5%
736 1
0.5%
673 1
0.5%
662 1
0.5%
586 1
0.5%

죄가안됨
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2352941
Minimum0
Maximum94
Zeros156
Zeros (%)83.4%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T07:20:28.278268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.7
Maximum94
Range94
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.4377875
Coefficient of variation (CV)6.0210661
Kurtosis132.36959
Mean1.2352941
Median Absolute Deviation (MAD)0
Skewness10.904189
Sum231
Variance55.320683
MonotonicityNot monotonic
2023-12-13T07:20:28.375422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 156
83.4%
1 13
 
7.0%
2 5
 
2.7%
6 2
 
1.1%
4 2
 
1.1%
5 2
 
1.1%
7 1
 
0.5%
94 1
 
0.5%
3 1
 
0.5%
23 1
 
0.5%
Other values (3) 3
 
1.6%
ValueCountFrequency (%)
0 156
83.4%
1 13
 
7.0%
2 5
 
2.7%
3 1
 
0.5%
4 2
 
1.1%
5 2
 
1.1%
6 2
 
1.1%
7 1
 
0.5%
12 1
 
0.5%
14 1
 
0.5%
ValueCountFrequency (%)
94 1
0.5%
25 1
0.5%
23 1
0.5%
14 1
0.5%
12 1
0.5%
7 1
0.5%
6 2
1.1%
5 2
1.1%
4 2
1.1%
3 1
0.5%

공소권없음
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.1016
Minimum0
Maximum7103
Zeros67
Zeros (%)35.8%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T07:20:28.482855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38.5
95-th percentile126.2
Maximum7103
Range7103
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation717.28641
Coefficient of variation (CV)6.7603729
Kurtosis85.971172
Mean106.1016
Median Absolute Deviation (MAD)2
Skewness9.2090716
Sum19841
Variance514499.79
MonotonicityNot monotonic
2023-12-13T07:20:28.595035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 67
35.8%
1 20
 
10.7%
2 14
 
7.5%
6 10
 
5.3%
5 7
 
3.7%
10 6
 
3.2%
4 6
 
3.2%
3 6
 
3.2%
8 6
 
3.2%
9 5
 
2.7%
Other values (36) 40
21.4%
ValueCountFrequency (%)
0 67
35.8%
1 20
 
10.7%
2 14
 
7.5%
3 6
 
3.2%
4 6
 
3.2%
5 7
 
3.7%
6 10
 
5.3%
7 4
 
2.1%
8 6
 
3.2%
9 5
 
2.7%
ValueCountFrequency (%)
7103 1
0.5%
6661 1
0.5%
964 1
0.5%
754 1
0.5%
732 1
0.5%
569 1
0.5%
568 1
0.5%
333 1
0.5%
236 1
0.5%
128 1
0.5%

기소중지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.518717
Minimum0
Maximum2018
Zeros71
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T07:20:28.718888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q311
95-th percentile80.8
Maximum2018
Range2018
Interquartile range (IQR)11

Descriptive statistics

Standard deviation150.89815
Coefficient of variation (CV)5.9132342
Kurtosis165.83069
Mean25.518717
Median Absolute Deviation (MAD)2
Skewness12.563828
Sum4772
Variance22770.251
MonotonicityNot monotonic
2023-12-13T07:20:28.871072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 71
38.0%
1 21
 
11.2%
2 14
 
7.5%
4 7
 
3.7%
3 5
 
2.7%
7 5
 
2.7%
6 4
 
2.1%
8 4
 
2.1%
5 4
 
2.1%
12 4
 
2.1%
Other values (37) 48
25.7%
ValueCountFrequency (%)
0 71
38.0%
1 21
 
11.2%
2 14
 
7.5%
3 5
 
2.7%
4 7
 
3.7%
5 4
 
2.1%
6 4
 
2.1%
7 5
 
2.7%
8 4
 
2.1%
10 3
 
1.6%
ValueCountFrequency (%)
2018 1
0.5%
319 1
0.5%
175 1
0.5%
154 1
0.5%
153 1
0.5%
111 1
0.5%
107 1
0.5%
95 1
0.5%
86 1
0.5%
82 1
0.5%

참고인중지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3475936
Minimum0
Maximum637
Zeros130
Zeros (%)69.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T07:20:29.012272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile9
Maximum637
Range637
Interquartile range (IQR)1

Descriptive statistics

Standard deviation46.757347
Coefficient of variation (CV)8.7436239
Kurtosis181.88551
Mean5.3475936
Median Absolute Deviation (MAD)0
Skewness13.401941
Sum1000
Variance2186.2495
MonotonicityNot monotonic
2023-12-13T07:20:29.132004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 130
69.5%
1 14
 
7.5%
4 8
 
4.3%
2 7
 
3.7%
5 5
 
2.7%
3 4
 
2.1%
6 3
 
1.6%
7 3
 
1.6%
9 2
 
1.1%
8 2
 
1.1%
Other values (8) 9
 
4.8%
ValueCountFrequency (%)
0 130
69.5%
1 14
 
7.5%
2 7
 
3.7%
3 4
 
2.1%
4 8
 
4.3%
5 5
 
2.7%
6 3
 
1.6%
7 3
 
1.6%
8 2
 
1.1%
9 2
 
1.1%
ValueCountFrequency (%)
637 1
0.5%
41 1
0.5%
33 1
0.5%
28 1
0.5%
27 1
0.5%
19 2
1.1%
14 1
0.5%
12 1
0.5%
9 2
1.1%
8 2
1.1%

Interactions

2023-12-13T07:20:23.501960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:13.213862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:14.178798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:14.987545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:15.936055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:16.894077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:17.977766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:19.171157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:20.555327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:21.553937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:22.611904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:23.583191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:13.276645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:14.246860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:15.057586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:16.010501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:16.995230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:18.071258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:19.267122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:20.650636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:21.683889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:22.689710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:23.657904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:13.341145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:14.314760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:15.131879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:16.088491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:17.081352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:18.194813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:19.360545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:20.726259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:21.781871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:22.768588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:23.767038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:13.404723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:14.380558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:15.219158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:16.167944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:17.168172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:18.312156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:19.479859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:20.807147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:21.902886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:22.834369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:23.858335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:13.470813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:14.449836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:15.304773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:16.250484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:17.289277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:18.441507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:19.589716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:20.918861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:22.026362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:22.908191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:23.951219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:13.761876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:14.527557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:15.375392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:16.337343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:17.377186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:18.557418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:19.990565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:21.017500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:22.131495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:22.981220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:24.031196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:13.825200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:14.593705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:15.474882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:16.412968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:17.477648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:18.642622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:20.068663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:21.099610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:22.211944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:23.060006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:24.117312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:13.895484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:14.674361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:15.565946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:16.528006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:17.586059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:18.768455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:20.183171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:21.194907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:22.296791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:23.155232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:24.206974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:13.963758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:14.740871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:15.663739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:16.603522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:17.675263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:18.849975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:20.263571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:21.271507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:22.368629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:23.226357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:24.305972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:14.036172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:14.821290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:15.756875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:16.689928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:17.767308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:18.962434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:20.356106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:21.371025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:22.452057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:23.312906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:24.407160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:14.109138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:14.906093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:15.853969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:16.788073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:17.878876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:19.075397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:20.459160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:21.465125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:22.535431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:20:23.403009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:20:29.218153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구공판_구속구공판_불구속구약식소년보호송치가정보호송치성매매보호송치아동보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지
구공판_구속1.0000.8750.4040.6180.0001.0000.0000.9470.7530.0000.0000.7121.000
구공판_불구속0.8751.0000.5850.6870.2831.0000.3620.7730.7150.4920.1170.6721.000
구약식0.4040.5851.0000.8280.8200.0000.6670.6450.6610.7650.7030.6830.439
소년보호송치0.6180.6870.8281.0000.9870.0000.6680.8160.9590.8370.7970.7970.882
가정보호송치0.0000.2830.8200.9871.0000.0000.7370.5180.7380.7380.8200.0000.000
성매매보호송치1.0001.0000.0000.0000.0001.0000.0000.4620.0000.0000.0000.0000.000
아동보호송치0.0000.3620.6670.6680.7370.0001.0000.7030.4520.6610.5410.0000.000
기소유예0.9470.7730.6450.8160.5180.4620.7031.0000.8920.4280.3100.7121.000
혐의없음0.7530.7150.6610.9590.7380.0000.4520.8921.0000.3980.5910.9411.000
죄가안됨0.0000.4920.7650.8370.7380.0000.6610.4280.3981.0000.4880.0000.000
공소권없음0.0000.1170.7030.7970.8200.0000.5410.3100.5910.4881.0000.7380.000
기소중지0.7120.6720.6830.7970.0000.0000.0000.7120.9410.0000.7381.0001.000
참고인중지1.0001.0000.4390.8820.0000.0000.0001.0001.0000.0000.0001.0001.000
2023-12-13T07:20:29.355665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아동보호송치성매매보호송치
아동보호송치1.0000.000
성매매보호송치0.0001.000
2023-12-13T07:20:29.460001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구공판_구속구공판_불구속구약식소년보호송치가정보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지성매매보호송치아동보호송치
구공판_구속1.0000.7130.2770.4430.2130.4490.4590.2520.3800.5170.5240.9920.000
구공판_불구속0.7131.0000.6060.5190.2150.6800.7020.3440.6020.7020.5470.9950.301
구약식0.2770.6061.0000.5000.2150.8070.7500.3630.6760.6040.4110.0000.508
소년보호송치0.4430.5190.5001.0000.3350.5980.5600.4930.4670.5520.3710.0000.527
가정보호송치0.2130.2150.2150.3351.0000.2150.2070.3270.2530.1470.2140.0000.729
기소유예0.4490.6800.8070.5980.2151.0000.7340.3710.6590.6880.4550.5570.332
혐의없음0.4590.7020.7500.5600.2070.7341.0000.3830.7390.6930.5970.0000.381
죄가안됨0.2520.3440.3630.4930.3270.3710.3831.0000.3740.3560.3280.0000.590
공소권없음0.3800.6020.6760.4670.2530.6590.7390.3741.0000.6790.5000.0000.480
기소중지0.5170.7020.6040.5520.1470.6880.6930.3560.6791.0000.5590.0000.000
참고인중지0.5240.5470.4110.3710.2140.4550.5970.3280.5000.5591.0000.0000.000
성매매보호송치0.9920.9950.0000.0000.0000.5570.0000.0000.0000.0000.0001.0000.000
아동보호송치0.0000.3010.5080.5270.7290.3320.3810.5900.4800.0000.0000.0001.000

Missing values

2023-12-13T07:20:24.564229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:20:24.766412image/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
0절도2603901364247000232686461161074
1불법사용0031000310010
2침입절도274071000030100740
3장물1517431000044550340
4사기56726851632710004256787625692018637
5컴퓨터등사용사기9303130002015606232
6부당이득0000000040000
7편의시설부정이용001600001790041
8전기통신금융사기피해금환급에관한특별법65352810002214505250
9보험사기방지특별법07332000100120020
범죄분류구공판_구속구공판_불구속구약식소년보호송치가정보호송치성매매보호송치아동보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지
177통신비밀보호법01300000100100
178특가법(도주차량)45447300014520651
179특허법005000006201310
180폐기물관리법0244000012210110
181풍속영업의 규제에 관한법률05120000800000
182학교보건법0150000050100
183학원의 설립운영 및 과외교습에 관한법률001800001180000
184화물자동차 운수사업법00110000014210600
185화학물질관리법356225000056150000
186기타특별법735081528410157875551037228