Overview

Dataset statistics

Number of variables12
Number of observations81
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.6 KiB
Average record size in memory108.6 B

Variable types

Text1
Numeric10
Categorical1

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 소년범죄자의 공범관계에 관한 통계임
Author대검찰청
URLhttps://www.data.go.kr/data/15084829/fileData.do

Alerts

단독범 is highly overall correlated with 학교동창 and 6 other fieldsHigh correlation
학교동창 is highly overall correlated with 단독범 and 6 other fieldsHigh correlation
교도소_소년원동료 is highly overall correlated with 친인척 and 2 other fieldsHigh correlation
직장동료 is highly overall correlated with 기타 and 1 other fieldsHigh correlation
친인척 is highly overall correlated with 단독범 and 7 other fieldsHigh correlation
동네친구 is highly overall correlated with 단독범 and 6 other fieldsHigh correlation
고향친구 is highly overall correlated with 단독범 and 7 other fieldsHigh correlation
애인 is highly overall correlated with 단독범 and 6 other fieldsHigh correlation
기타 is highly overall correlated with 단독범 and 8 other fieldsHigh correlation
미상 is highly overall correlated with 단독범 and 6 other fieldsHigh correlation
군동료 is highly overall correlated with 직장동료High correlation
군동료 is highly imbalanced (90.4%)Imbalance
범죄분류 has unique valuesUnique
단독범 has 11 (13.6%) zerosZeros
학교동창 has 33 (40.7%) zerosZeros
교도소_소년원동료 has 71 (87.7%) zerosZeros
직장동료 has 59 (72.8%) zerosZeros
친인척 has 55 (67.9%) zerosZeros
동네친구 has 30 (37.0%) zerosZeros
고향친구 has 46 (56.8%) zerosZeros
애인 has 49 (60.5%) zerosZeros
기타 has 22 (27.2%) zerosZeros
미상 has 18 (22.2%) zerosZeros

Reproduction

Analysis started2023-12-11 23:33:17.194856
Analysis finished2023-12-11 23:33:27.864275
Duration10.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct81
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size780.0 B
2023-12-12T08:33:28.152794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length6.7530864
Min length2

Characters and Unicode

Total characters547
Distinct characters168
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)100.0%

Sample

1st row절도
2nd row장물
3rd row사기
4th row횡령
5th row배임
ValueCountFrequency (%)
절도 1
 
1.2%
개발제한구역의지정및관리에관한특별조치법 1
 
1.2%
상표법 1
 
1.2%
사행행위등규제및처벌특례법 1
 
1.2%
물환경보전법 1
 
1.2%
마약류관리에관한법률 1
 
1.2%
도로법 1
 
1.2%
도로교통법 1
 
1.2%
대부업등의등록및금융이용자보호에관한법률 1
 
1.2%
대기환경보전법 1
 
1.2%
Other values (71) 71
87.7%
2023-12-12T08:33:28.588636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40
 
7.3%
16
 
2.9%
13
 
2.4%
12
 
2.2%
12
 
2.2%
11
 
2.0%
10
 
1.8%
10
 
1.8%
8
 
1.5%
8
 
1.5%
Other values (158) 407
74.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 530
96.9%
Other Punctuation 7
 
1.3%
Open Punctuation 5
 
0.9%
Close Punctuation 5
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
40
 
7.5%
16
 
3.0%
13
 
2.5%
12
 
2.3%
12
 
2.3%
11
 
2.1%
10
 
1.9%
10
 
1.9%
8
 
1.5%
8
 
1.5%
Other values (154) 390
73.6%
Other Punctuation
ValueCountFrequency (%)
· 5
71.4%
, 2
 
28.6%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 530
96.9%
Common 17
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
40
 
7.5%
16
 
3.0%
13
 
2.5%
12
 
2.3%
12
 
2.3%
11
 
2.1%
10
 
1.9%
10
 
1.9%
8
 
1.5%
8
 
1.5%
Other values (154) 390
73.6%
Common
ValueCountFrequency (%)
· 5
29.4%
( 5
29.4%
) 5
29.4%
, 2
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 530
96.9%
ASCII 12
 
2.2%
None 5
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
40
 
7.5%
16
 
3.0%
13
 
2.5%
12
 
2.3%
12
 
2.3%
11
 
2.1%
10
 
1.9%
10
 
1.9%
8
 
1.5%
8
 
1.5%
Other values (154) 390
73.6%
None
ValueCountFrequency (%)
· 5
100.0%
ASCII
ValueCountFrequency (%)
( 5
41.7%
) 5
41.7%
, 2
 
16.7%

단독범
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean493.33333
Minimum0
Maximum6716
Zeros11
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T08:33:28.737974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median32
Q3213
95-th percentile2900
Maximum6716
Range6716
Interquartile range (IQR)210

Descriptive statistics

Standard deviation1287.9097
Coefficient of variation (CV)2.6106277
Kurtosis12.204568
Mean493.33333
Median Absolute Deviation (MAD)32
Skewness3.4953006
Sum39960
Variance1658711.3
MonotonicityNot monotonic
2023-12-12T08:33:28.870901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
13.6%
3 5
 
6.2%
1 5
 
6.2%
2 3
 
3.7%
4 3
 
3.7%
9 3
 
3.7%
8 2
 
2.5%
11 2
 
2.5%
39 2
 
2.5%
7 2
 
2.5%
Other values (43) 43
53.1%
ValueCountFrequency (%)
0 11
13.6%
1 5
6.2%
2 3
 
3.7%
3 5
6.2%
4 3
 
3.7%
5 1
 
1.2%
7 2
 
2.5%
8 2
 
2.5%
9 3
 
3.7%
11 2
 
2.5%
ValueCountFrequency (%)
6716 1
1.2%
5726 1
1.2%
5560 1
1.2%
3972 1
1.2%
2900 1
1.2%
2596 1
1.2%
2539 1
1.2%
1184 1
1.2%
1052 1
1.2%
955 1
1.2%

학교동창
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.76543
Minimum0
Maximum5287
Zeros33
Zeros (%)40.7%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T08:33:28.994151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q327
95-th percentile261
Maximum5287
Range5287
Interquartile range (IQR)27

Descriptive statistics

Standard deviation623.8464
Coefficient of variation (CV)4.7707287
Kurtosis60.284762
Mean130.76543
Median Absolute Deviation (MAD)3
Skewness7.4666594
Sum10592
Variance389184.33
MonotonicityNot monotonic
2023-12-12T08:33:29.113954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 33
40.7%
4 4
 
4.9%
2 4
 
4.9%
8 3
 
3.7%
5 2
 
2.5%
9 2
 
2.5%
3 2
 
2.5%
1 2
 
2.5%
5287 1
 
1.2%
121 1
 
1.2%
Other values (27) 27
33.3%
ValueCountFrequency (%)
0 33
40.7%
1 2
 
2.5%
2 4
 
4.9%
3 2
 
2.5%
4 4
 
4.9%
5 2
 
2.5%
6 1
 
1.2%
8 3
 
3.7%
9 2
 
2.5%
11 1
 
1.2%
ValueCountFrequency (%)
5287 1
1.2%
1441 1
1.2%
1435 1
1.2%
385 1
1.2%
261 1
1.2%
207 1
1.2%
198 1
1.2%
190 1
1.2%
189 1
1.2%
121 1
1.2%

교도소_소년원동료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3703704
Minimum0
Maximum134
Zeros71
Zeros (%)87.7%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T08:33:29.260481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum134
Range134
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.183416
Coefficient of variation (CV)6.4055035
Kurtosis73.101243
Mean2.3703704
Median Absolute Deviation (MAD)0
Skewness8.4097193
Sum192
Variance230.53611
MonotonicityNot monotonic
2023-12-12T08:33:29.362834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 71
87.7%
3 2
 
2.5%
7 2
 
2.5%
1 2
 
2.5%
134 1
 
1.2%
28 1
 
1.2%
2 1
 
1.2%
6 1
 
1.2%
ValueCountFrequency (%)
0 71
87.7%
1 2
 
2.5%
2 1
 
1.2%
3 2
 
2.5%
6 1
 
1.2%
7 2
 
2.5%
28 1
 
1.2%
134 1
 
1.2%
ValueCountFrequency (%)
134 1
 
1.2%
28 1
 
1.2%
7 2
 
2.5%
6 1
 
1.2%
3 2
 
2.5%
2 1
 
1.2%
1 2
 
2.5%
0 71
87.7%

직장동료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2098765
Minimum0
Maximum19
Zeros59
Zeros (%)72.8%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T08:33:29.461235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile10
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.29665
Coefficient of variation (CV)2.7247821
Kurtosis13.880746
Mean1.2098765
Median Absolute Deviation (MAD)0
Skewness3.6176135
Sum98
Variance10.867901
MonotonicityNot monotonic
2023-12-12T08:33:29.819315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 59
72.8%
1 11
 
13.6%
10 2
 
2.5%
3 2
 
2.5%
19 1
 
1.2%
4 1
 
1.2%
11 1
 
1.2%
5 1
 
1.2%
2 1
 
1.2%
6 1
 
1.2%
ValueCountFrequency (%)
0 59
72.8%
1 11
 
13.6%
2 1
 
1.2%
3 2
 
2.5%
4 1
 
1.2%
5 1
 
1.2%
6 1
 
1.2%
10 2
 
2.5%
11 1
 
1.2%
14 1
 
1.2%
ValueCountFrequency (%)
19 1
 
1.2%
14 1
 
1.2%
11 1
 
1.2%
10 2
 
2.5%
6 1
 
1.2%
5 1
 
1.2%
4 1
 
1.2%
3 2
 
2.5%
2 1
 
1.2%
1 11
13.6%

친인척
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1481481
Minimum0
Maximum109
Zeros55
Zeros (%)67.9%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T08:33:29.940797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum109
Range109
Interquartile range (IQR)1

Descriptive statistics

Standard deviation13.905135
Coefficient of variation (CV)4.4169252
Kurtosis44.403134
Mean3.1481481
Median Absolute Deviation (MAD)0
Skewness6.3557852
Sum255
Variance193.35278
MonotonicityNot monotonic
2023-12-12T08:33:30.046194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 55
67.9%
1 12
 
14.8%
2 5
 
6.2%
3 3
 
3.7%
109 1
 
1.2%
17 1
 
1.2%
49 1
 
1.2%
40 1
 
1.2%
5 1
 
1.2%
4 1
 
1.2%
ValueCountFrequency (%)
0 55
67.9%
1 12
 
14.8%
2 5
 
6.2%
3 3
 
3.7%
4 1
 
1.2%
5 1
 
1.2%
17 1
 
1.2%
40 1
 
1.2%
49 1
 
1.2%
109 1
 
1.2%
ValueCountFrequency (%)
109 1
 
1.2%
49 1
 
1.2%
40 1
 
1.2%
17 1
 
1.2%
5 1
 
1.2%
4 1
 
1.2%
3 3
 
3.7%
2 5
 
6.2%
1 12
 
14.8%
0 55
67.9%

군동료
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size780.0 B
0
80 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 80
98.8%
1 1
 
1.2%

Length

2023-12-12T08:33:30.163559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:33:30.265187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 80
98.8%
1 1
 
1.2%

동네친구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.2716
Minimum0
Maximum6648
Zeros30
Zeros (%)37.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T08:33:30.401688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q344
95-th percentile439
Maximum6648
Range6648
Interquartile range (IQR)44

Descriptive statistics

Standard deviation782.59207
Coefficient of variation (CV)4.6232921
Kurtosis60.585231
Mean169.2716
Median Absolute Deviation (MAD)4
Skewness7.4695912
Sum13711
Variance612450.35
MonotonicityNot monotonic
2023-12-12T08:33:30.524850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 30
37.0%
1 8
 
9.9%
4 4
 
4.9%
11 2
 
2.5%
6 2
 
2.5%
113 2
 
2.5%
76 1
 
1.2%
40 1
 
1.2%
16 1
 
1.2%
8 1
 
1.2%
Other values (29) 29
35.8%
ValueCountFrequency (%)
0 30
37.0%
1 8
 
9.9%
2 1
 
1.2%
3 1
 
1.2%
4 4
 
4.9%
5 1
 
1.2%
6 2
 
2.5%
8 1
 
1.2%
10 1
 
1.2%
11 2
 
2.5%
ValueCountFrequency (%)
6648 1
1.2%
1714 1
1.2%
1707 1
1.2%
864 1
1.2%
439 1
1.2%
290 1
1.2%
212 1
1.2%
193 1
1.2%
178 1
1.2%
166 1
1.2%

고향친구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0123457
Minimum0
Maximum313
Zeros46
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T08:33:30.641968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile21
Maximum313
Range313
Interquartile range (IQR)3

Descriptive statistics

Standard deviation37.773831
Coefficient of variation (CV)4.1913429
Kurtosis54.031179
Mean9.0123457
Median Absolute Deviation (MAD)0
Skewness6.9726303
Sum730
Variance1426.8623
MonotonicityNot monotonic
2023-12-12T08:33:30.740161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 46
56.8%
2 6
 
7.4%
5 5
 
6.2%
1 5
 
6.2%
3 4
 
4.9%
9 3
 
3.7%
11 2
 
2.5%
4 2
 
2.5%
313 1
 
1.2%
17 1
 
1.2%
Other values (6) 6
 
7.4%
ValueCountFrequency (%)
0 46
56.8%
1 5
 
6.2%
2 6
 
7.4%
3 4
 
4.9%
4 2
 
2.5%
5 5
 
6.2%
6 1
 
1.2%
9 3
 
3.7%
10 1
 
1.2%
11 2
 
2.5%
ValueCountFrequency (%)
313 1
 
1.2%
87 1
 
1.2%
84 1
 
1.2%
81 1
 
1.2%
21 1
 
1.2%
17 1
 
1.2%
11 2
2.5%
10 1
 
1.2%
9 3
3.7%
6 1
 
1.2%

애인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7777778
Minimum0
Maximum184
Zeros49
Zeros (%)60.5%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T08:33:30.841330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile18
Maximum184
Range184
Interquartile range (IQR)2

Descriptive statistics

Standard deviation23.086251
Coefficient of variation (CV)3.9956973
Kurtosis46.449957
Mean5.7777778
Median Absolute Deviation (MAD)0
Skewness6.4521349
Sum468
Variance532.975
MonotonicityNot monotonic
2023-12-12T08:33:30.946294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 49
60.5%
2 6
 
7.4%
3 6
 
7.4%
1 6
 
7.4%
4 5
 
6.2%
184 1
 
1.2%
80 1
 
1.2%
18 1
 
1.2%
7 1
 
1.2%
52 1
 
1.2%
Other values (4) 4
 
4.9%
ValueCountFrequency (%)
0 49
60.5%
1 6
 
7.4%
2 6
 
7.4%
3 6
 
7.4%
4 5
 
6.2%
7 1
 
1.2%
8 1
 
1.2%
9 1
 
1.2%
16 1
 
1.2%
18 1
 
1.2%
ValueCountFrequency (%)
184 1
 
1.2%
80 1
 
1.2%
52 1
 
1.2%
38 1
 
1.2%
18 1
 
1.2%
16 1
 
1.2%
9 1
 
1.2%
8 1
 
1.2%
7 1
 
1.2%
4 5
6.2%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44
Minimum0
Maximum1243
Zeros22
Zeros (%)27.2%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T08:33:31.088366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q321
95-th percentile102
Maximum1243
Range1243
Interquartile range (IQR)21

Descriptive statistics

Standard deviation156.02692
Coefficient of variation (CV)3.5460664
Kurtosis45.092494
Mean44
Median Absolute Deviation (MAD)3
Skewness6.2953898
Sum3564
Variance24344.4
MonotonicityNot monotonic
2023-12-12T08:33:31.215598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 22
27.2%
1 10
12.3%
3 7
 
8.6%
2 6
 
7.4%
5 3
 
3.7%
12 2
 
2.5%
14 2
 
2.5%
4 2
 
2.5%
41 2
 
2.5%
7 2
 
2.5%
Other values (23) 23
28.4%
ValueCountFrequency (%)
0 22
27.2%
1 10
12.3%
2 6
 
7.4%
3 7
 
8.6%
4 2
 
2.5%
5 3
 
3.7%
7 2
 
2.5%
9 1
 
1.2%
11 1
 
1.2%
12 2
 
2.5%
ValueCountFrequency (%)
1243 1
1.2%
432 1
1.2%
408 1
1.2%
384 1
1.2%
102 1
1.2%
88 1
1.2%
81 1
1.2%
77 1
1.2%
68 1
1.2%
57 1
1.2%

미상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.358025
Minimum0
Maximum1014
Zeros18
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T08:33:31.372662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q322
95-th percentile191
Maximum1014
Range1014
Interquartile range (IQR)21

Descriptive statistics

Standard deviation130.1071
Coefficient of variation (CV)3.3057325
Kurtosis40.773121
Mean39.358025
Median Absolute Deviation (MAD)3
Skewness5.9368133
Sum3188
Variance16927.858
MonotonicityNot monotonic
2023-12-12T08:33:31.482889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 19
23.5%
0 18
22.2%
3 6
 
7.4%
11 3
 
3.7%
6 2
 
2.5%
24 2
 
2.5%
2 2
 
2.5%
12 2
 
2.5%
4 2
 
2.5%
9 1
 
1.2%
Other values (24) 24
29.6%
ValueCountFrequency (%)
0 18
22.2%
1 19
23.5%
2 2
 
2.5%
3 6
 
7.4%
4 2
 
2.5%
6 2
 
2.5%
7 1
 
1.2%
9 1
 
1.2%
10 1
 
1.2%
11 3
 
3.7%
ValueCountFrequency (%)
1014 1
1.2%
401 1
1.2%
345 1
1.2%
299 1
1.2%
191 1
1.2%
113 1
1.2%
72 1
1.2%
71 1
1.2%
70 1
1.2%
60 1
1.2%

Interactions

2023-12-12T08:33:26.615069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:17.536941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:18.475396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:19.196792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:20.104439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:21.061402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:22.121552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:23.515275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:24.513108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:25.682432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:26.713367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:17.610415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:18.544066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:19.271828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:20.199115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:21.149915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:22.258918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:23.625882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:24.624046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:25.784932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:26.808227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:17.902046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:18.619689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:19.337958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:20.278768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:21.238349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:22.397754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:23.708983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:24.719305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:25.883895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:26.897753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:17.970843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:18.700967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:19.412594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:20.366829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:21.349532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:22.506945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:23.792536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:24.832906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:25.975940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:26.981373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:18.048701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:18.777359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:19.492279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:20.453984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:21.451644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:22.617267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:23.901995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:24.943033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:26.087030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:27.060418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:18.129722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:18.841715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:19.629175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:20.550698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:21.561446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:22.720563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:24.007965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:25.078126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:26.177302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:27.159554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:18.196754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:18.911045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:19.715593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:20.648658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:21.671564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:22.811885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:24.124156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:25.233031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:26.275641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:27.277328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:18.264384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:18.987906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:19.808862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:20.753633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:21.777531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:22.914982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:24.207677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:25.342550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:26.358634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:27.378436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:18.337401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:19.063402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:19.906328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:20.870822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:21.896019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:23.036969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:24.310295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:25.470001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:26.447411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:27.458512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:18.408497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:19.132057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:20.016026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:20.976724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:22.036524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:23.151811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:24.409368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:25.577807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:26.534968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:33:31.565845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
범죄분류단독범학교동창교도소_소년원동료직장동료친인척군동료동네친구고향친구애인기타미상
범죄분류1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
단독범1.0001.0000.8100.8670.9140.9030.0000.8960.8300.8960.8300.820
학교동창1.0000.8101.0000.9390.9001.0000.0001.0000.9941.0000.9940.766
교도소_소년원동료1.0000.8670.9391.0000.8311.0000.0001.0000.9761.0000.9760.704
직장동료1.0000.9140.9000.8311.0000.7981.0000.8661.0000.8661.0000.642
친인척1.0000.9031.0001.0000.7981.0000.0001.0001.0001.0001.0000.976
군동료1.0000.0000.0000.0001.0000.0001.0000.0000.0000.0000.0000.000
동네친구1.0000.8961.0001.0000.8661.0000.0001.0001.0001.0001.0000.791
고향친구1.0000.8300.9940.9761.0001.0000.0001.0001.0001.0001.0000.747
애인1.0000.8961.0001.0000.8661.0000.0001.0001.0001.0001.0000.791
기타1.0000.8300.9940.9761.0001.0000.0001.0001.0001.0001.0000.747
미상1.0000.8200.7660.7040.6420.9760.0000.7910.7470.7910.7471.000
2023-12-12T08:33:31.702865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단독범학교동창교도소_소년원동료직장동료친인척동네친구고향친구애인기타미상군동료
단독범1.0000.8310.4100.4560.5910.7980.7520.6590.8230.7940.000
학교동창0.8311.0000.4860.4060.6730.9000.8010.7750.8530.7690.000
교도소_소년원동료0.4100.4861.0000.3600.5050.4920.5860.4660.5060.4550.000
직장동료0.4560.4060.3601.0000.3830.4610.4120.4910.5040.4200.968
친인척0.5910.6730.5050.3831.0000.6740.6260.6690.5890.5350.000
동네친구0.7980.9000.4920.4610.6741.0000.8500.7590.8500.7270.000
고향친구0.7520.8010.5860.4120.6260.8501.0000.6930.7480.6760.000
애인0.6590.7750.4660.4910.6690.7590.6931.0000.7210.6420.000
기타0.8230.8530.5060.5040.5890.8500.7480.7211.0000.7610.000
미상0.7940.7690.4550.4200.5350.7270.6760.6420.7611.0000.000
군동료0.0000.0000.0000.9680.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T08:33:27.597450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:33:27.805007image/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

범죄분류단독범학교동창교도소_소년원동료직장동료친인척군동료동네친구고향친구애인기타미상
0절도5726528713419109066483131841243345
1장물2865600006742217
2사기397238528101708648480384401
3횡령95519003101669184141
4배임00000000001
5손괴739610120100541726
6살인92002010211
7강도32353030121572810
8방화45210000441331
9성폭력253920704201932144370
범죄분류단독범학교동창교도소_소년원동료직장동료친인척군동료동네친구고향친구애인기타미상
71전자금융거래법1658100016505722
72정보통신망이용촉진및정보보호등에관한법률49827000040214271
73조세범처벌법00000000003
74주민등록법5849000001131133012
75청소년보호법594670141176328115
76출입국관리법30010000050
77특가법(도주차량)1154000030046
78풍속영업의규제에관한법률00010000000
79학원의설립운영및과외교습에관한법률10000000000
80기타특별법293510110138924136