Overview

Dataset statistics

Number of variables12
Number of observations162
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.1 KiB
Average record size in memory107.8 B

Variable types

Text1
Numeric11

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 범죄자의 공범관계에 관한 통계임. (단위 : 건)
Author대검찰청
URLhttps://www.data.go.kr/data/15085882/fileData.do

Alerts

단독범 is highly overall correlated with 학교동창 and 8 other fieldsHigh correlation
학교동창 is highly overall correlated with 단독범 and 9 other fieldsHigh correlation
교도소_소년원동료 is highly overall correlated with 학교동창 and 3 other fieldsHigh correlation
직장동료 is highly overall correlated with 단독범 and 8 other fieldsHigh correlation
친인척 is highly overall correlated with 단독범 and 8 other fieldsHigh correlation
군동료 is highly overall correlated with 단독범 and 7 other fieldsHigh correlation
동네친구 is highly overall correlated with 단독범 and 9 other fieldsHigh correlation
고향친구 is highly overall correlated with 단독범 and 9 other fieldsHigh correlation
애인 is highly overall correlated with 단독범 and 9 other fieldsHigh correlation
기타 is highly overall correlated with 단독범 and 8 other fieldsHigh correlation
미상 is highly overall correlated with 단독범 and 7 other fieldsHigh correlation
범죄분류 has unique valuesUnique
학교동창 has 75 (46.3%) zerosZeros
교도소_소년원동료 has 132 (81.5%) zerosZeros
직장동료 has 17 (10.5%) zerosZeros
친인척 has 32 (19.8%) zerosZeros
군동료 has 128 (79.0%) zerosZeros
동네친구 has 46 (28.4%) zerosZeros
고향친구 has 70 (43.2%) zerosZeros
애인 has 70 (43.2%) zerosZeros
기타 has 3 (1.9%) zerosZeros

Reproduction

Analysis started2023-12-12 14:25:35.349548
Analysis finished2023-12-12 14:25:48.817469
Duration13.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct162
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-12T23:25:49.018435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length19
Mean length8.037037
Min length2

Characters and Unicode

Total characters1302
Distinct characters225
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

Unique162 ?
Unique (%)100.0%

Sample

1st row절도
2nd row장물
3rd row사기
4th row횡령
5th row배임
ValueCountFrequency (%)
절도 1
 
0.6%
약사법 1
 
0.6%
사행행위등규제및처벌특례법 1
 
0.6%
성매매알선등행위의처벌에관한법률 1
 
0.6%
산림자원의조성및관리에관한법률 1
 
0.6%
산업안전보건법 1
 
0.6%
산지관리법 1
 
0.6%
상표법 1
 
0.6%
석유및석유대체연료사업법 1
 
0.6%
선박안전법 1
 
0.6%
Other values (152) 152
93.8%
2023-12-12T23:25:49.408590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
114
 
8.8%
67
 
5.1%
41
 
3.1%
40
 
3.1%
34
 
2.6%
31
 
2.4%
27
 
2.1%
26
 
2.0%
23
 
1.8%
19
 
1.5%
Other values (215) 880
67.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1268
97.4%
Open Punctuation 12
 
0.9%
Close Punctuation 12
 
0.9%
Other Punctuation 10
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
114
 
9.0%
67
 
5.3%
41
 
3.2%
40
 
3.2%
34
 
2.7%
31
 
2.4%
27
 
2.1%
26
 
2.1%
23
 
1.8%
19
 
1.5%
Other values (211) 846
66.7%
Other Punctuation
ValueCountFrequency (%)
, 8
80.0%
· 2
 
20.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1268
97.4%
Common 34
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
114
 
9.0%
67
 
5.3%
41
 
3.2%
40
 
3.2%
34
 
2.7%
31
 
2.4%
27
 
2.1%
26
 
2.1%
23
 
1.8%
19
 
1.5%
Other values (211) 846
66.7%
Common
ValueCountFrequency (%)
( 12
35.3%
) 12
35.3%
, 8
23.5%
· 2
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1268
97.4%
ASCII 32
 
2.5%
None 2
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
114
 
9.0%
67
 
5.3%
41
 
3.2%
40
 
3.2%
34
 
2.7%
31
 
2.4%
27
 
2.1%
26
 
2.1%
23
 
1.8%
19
 
1.5%
Other values (211) 846
66.7%
ASCII
ValueCountFrequency (%)
( 12
37.5%
) 12
37.5%
, 8
25.0%
None
ValueCountFrequency (%)
· 2
100.0%

단독범
Real number (ℝ)

HIGH CORRELATION 

Distinct155
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8517.4815
Minimum0
Maximum184691
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T23:25:49.562299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.35
Q1208.25
median783
Q33207.25
95-th percentile32053.05
Maximum184691
Range184691
Interquartile range (IQR)2999

Descriptive statistics

Standard deviation27792.214
Coefficient of variation (CV)3.2629614
Kurtosis29.175529
Mean8517.4815
Median Absolute Deviation (MAD)702
Skewness5.2705346
Sum1379832
Variance7.7240714 × 108
MonotonicityNot monotonic
2023-12-12T23:25:50.144382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450 2
 
1.2%
129 2
 
1.2%
34 2
 
1.2%
187 2
 
1.2%
823 2
 
1.2%
172 2
 
1.2%
43 2
 
1.2%
169 1
 
0.6%
795 1
 
0.6%
304 1
 
0.6%
Other values (145) 145
89.5%
ValueCountFrequency (%)
0 1
0.6%
2 1
0.6%
3 1
0.6%
5 1
0.6%
13 1
0.6%
15 1
0.6%
18 1
0.6%
20 1
0.6%
27 1
0.6%
34 2
1.2%
ValueCountFrequency (%)
184691 1
0.6%
183633 1
0.6%
179183 1
0.6%
126953 1
0.6%
79270 1
0.6%
54535 1
0.6%
48024 1
0.6%
37930 1
0.6%
32130 1
0.6%
30591 1
0.6%

학교동창
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.660494
Minimum0
Maximum6042
Zeros75
Zeros (%)46.3%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T23:25:50.305904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q313
95-th percentile210.6
Maximum6042
Range6042
Interquartile range (IQR)13

Descriptive statistics

Standard deviation536.421
Coefficient of variation (CV)6.0502822
Kurtosis98.130072
Mean88.660494
Median Absolute Deviation (MAD)1.5
Skewness9.4210403
Sum14363
Variance287747.49
MonotonicityNot monotonic
2023-12-12T23:25:50.467089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 75
46.3%
2 12
 
7.4%
1 6
 
3.7%
4 5
 
3.1%
3 5
 
3.1%
5 4
 
2.5%
6 3
 
1.9%
13 3
 
1.9%
37 2
 
1.2%
29 2
 
1.2%
Other values (39) 45
27.8%
ValueCountFrequency (%)
0 75
46.3%
1 6
 
3.7%
2 12
 
7.4%
3 5
 
3.1%
4 5
 
3.1%
5 4
 
2.5%
6 3
 
1.9%
7 2
 
1.2%
8 1
 
0.6%
9 2
 
1.2%
ValueCountFrequency (%)
6042 1
0.6%
2347 1
0.6%
2122 1
0.6%
805 1
0.6%
291 1
0.6%
273 1
0.6%
259 1
0.6%
231 1
0.6%
215 1
0.6%
127 1
0.6%

교도소_소년원동료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8703704
Minimum0
Maximum316
Zeros132
Zeros (%)81.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T23:25:50.642238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11.85
Maximum316
Range316
Interquartile range (IQR)0

Descriptive statistics

Standard deviation31.016353
Coefficient of variation (CV)6.3683766
Kurtosis77.289493
Mean4.8703704
Median Absolute Deviation (MAD)0
Skewness8.5621805
Sum789
Variance962.01415
MonotonicityNot monotonic
2023-12-12T23:25:50.778545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 132
81.5%
1 9
 
5.6%
2 6
 
3.7%
12 3
 
1.9%
8 2
 
1.2%
316 1
 
0.6%
219 1
 
0.6%
3 1
 
0.6%
25 1
 
0.6%
26 1
 
0.6%
Other values (5) 5
 
3.1%
ValueCountFrequency (%)
0 132
81.5%
1 9
 
5.6%
2 6
 
3.7%
3 1
 
0.6%
4 1
 
0.6%
6 1
 
0.6%
8 2
 
1.2%
9 1
 
0.6%
12 3
 
1.9%
15 1
 
0.6%
ValueCountFrequency (%)
316 1
 
0.6%
219 1
 
0.6%
93 1
 
0.6%
26 1
 
0.6%
25 1
 
0.6%
15 1
 
0.6%
12 3
1.9%
9 1
 
0.6%
8 2
1.2%
6 1
 
0.6%

직장동료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.15432
Minimum0
Maximum3864
Zeros17
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T23:25:50.947058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median28.5
Q395.75
95-th percentile751
Maximum3864
Range3864
Interquartile range (IQR)88.75

Descriptive statistics

Standard deviation453.11819
Coefficient of variation (CV)2.7603184
Kurtosis32.921121
Mean164.15432
Median Absolute Deviation (MAD)26.5
Skewness5.2142157
Sum26593
Variance205316.09
MonotonicityNot monotonic
2023-12-12T23:25:51.141119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
10.5%
11 6
 
3.7%
5 6
 
3.7%
2 5
 
3.1%
10 4
 
2.5%
8 4
 
2.5%
1 4
 
2.5%
7 4
 
2.5%
41 3
 
1.9%
16 3
 
1.9%
Other values (86) 106
65.4%
ValueCountFrequency (%)
0 17
10.5%
1 4
 
2.5%
2 5
 
3.1%
3 3
 
1.9%
4 2
 
1.2%
5 6
 
3.7%
6 1
 
0.6%
7 4
 
2.5%
8 4
 
2.5%
9 1
 
0.6%
ValueCountFrequency (%)
3864 1
0.6%
2390 1
0.6%
1900 1
0.6%
1662 1
0.6%
1641 1
0.6%
1523 1
0.6%
1001 1
0.6%
829 1
0.6%
756 1
0.6%
656 1
0.6%

친인척
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)40.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.092593
Minimum0
Maximum2118
Zeros32
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T23:25:51.316307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.25
median8
Q328.75
95-th percentile215.35
Maximum2118
Range2118
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation270.84936
Coefficient of variation (CV)3.7055651
Kurtosis38.748732
Mean73.092593
Median Absolute Deviation (MAD)8
Skewness6.0979461
Sum11841
Variance73359.376
MonotonicityNot monotonic
2023-12-12T23:25:51.483524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32
19.8%
2 17
 
10.5%
5 10
 
6.2%
1 9
 
5.6%
8 6
 
3.7%
4 5
 
3.1%
15 4
 
2.5%
3 4
 
2.5%
28 3
 
1.9%
29 3
 
1.9%
Other values (55) 69
42.6%
ValueCountFrequency (%)
0 32
19.8%
1 9
 
5.6%
2 17
10.5%
3 4
 
2.5%
4 5
 
3.1%
5 10
 
6.2%
6 2
 
1.2%
7 1
 
0.6%
8 6
 
3.7%
9 3
 
1.9%
ValueCountFrequency (%)
2118 1
0.6%
1856 1
0.6%
1646 1
0.6%
1112 1
0.6%
361 1
0.6%
350 1
0.6%
309 1
0.6%
227 1
0.6%
216 1
0.6%
203 1
0.6%

군동료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1728395
Minimum0
Maximum34
Zeros128
Zeros (%)79.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T23:25:51.639480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.95
Maximum34
Range34
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.2886246
Coefficient of variation (CV)3.6566168
Kurtosis40.559082
Mean1.1728395
Median Absolute Deviation (MAD)0
Skewness5.992394
Sum190
Variance18.392301
MonotonicityNot monotonic
2023-12-12T23:25:51.777174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 128
79.0%
2 10
 
6.2%
1 9
 
5.6%
5 3
 
1.9%
7 2
 
1.2%
3 2
 
1.2%
9 2
 
1.2%
33 1
 
0.6%
34 1
 
0.6%
6 1
 
0.6%
Other values (3) 3
 
1.9%
ValueCountFrequency (%)
0 128
79.0%
1 9
 
5.6%
2 10
 
6.2%
3 2
 
1.2%
4 1
 
0.6%
5 3
 
1.9%
6 1
 
0.6%
7 2
 
1.2%
9 2
 
1.2%
14 1
 
0.6%
ValueCountFrequency (%)
34 1
 
0.6%
33 1
 
0.6%
17 1
 
0.6%
14 1
 
0.6%
9 2
1.2%
7 2
1.2%
6 1
 
0.6%
5 3
1.9%
4 1
 
0.6%
3 2
1.2%

동네친구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228.62346
Minimum0
Maximum8980
Zeros46
Zeros (%)28.4%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T23:25:51.959922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.5
Q351.25
95-th percentile401.55
Maximum8980
Range8980
Interquartile range (IQR)51.25

Descriptive statistics

Standard deviation1035.6345
Coefficient of variation (CV)4.5298696
Kurtosis41.841633
Mean228.62346
Median Absolute Deviation (MAD)6.5
Skewness6.2437339
Sum37037
Variance1072538.7
MonotonicityNot monotonic
2023-12-12T23:25:52.143474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46
28.4%
2 17
 
10.5%
4 9
 
5.6%
12 4
 
2.5%
13 4
 
2.5%
9 4
 
2.5%
1 3
 
1.9%
8 3
 
1.9%
6 3
 
1.9%
10 3
 
1.9%
Other values (59) 66
40.7%
ValueCountFrequency (%)
0 46
28.4%
1 3
 
1.9%
2 17
 
10.5%
3 2
 
1.2%
4 9
 
5.6%
5 1
 
0.6%
6 3
 
1.9%
7 2
 
1.2%
8 3
 
1.9%
9 4
 
2.5%
ValueCountFrequency (%)
8980 1
0.6%
5865 1
0.6%
4886 1
0.6%
4857 1
0.6%
4020 1
0.6%
760 1
0.6%
509 1
0.6%
466 1
0.6%
402 1
0.6%
393 1
0.6%

고향친구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.666667
Minimum0
Maximum815
Zeros70
Zeros (%)43.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T23:25:52.292254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q312
95-th percentile106.4
Maximum815
Range815
Interquartile range (IQR)12

Descriptive statistics

Standard deviation129.33432
Coefficient of variation (CV)3.8416135
Kurtosis26.851019
Mean33.666667
Median Absolute Deviation (MAD)2
Skewness5.2464718
Sum5454
Variance16727.366
MonotonicityNot monotonic
2023-12-12T23:25:52.452355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 70
43.2%
2 13
 
8.0%
4 9
 
5.6%
3 9
 
5.6%
1 7
 
4.3%
12 4
 
2.5%
5 4
 
2.5%
13 3
 
1.9%
15 3
 
1.9%
39 3
 
1.9%
Other values (34) 37
22.8%
ValueCountFrequency (%)
0 70
43.2%
1 7
 
4.3%
2 13
 
8.0%
3 9
 
5.6%
4 9
 
5.6%
5 4
 
2.5%
6 2
 
1.2%
7 1
 
0.6%
8 1
 
0.6%
10 1
 
0.6%
ValueCountFrequency (%)
815 1
0.6%
762 1
0.6%
754 1
0.6%
704 1
0.6%
678 1
0.6%
140 1
0.6%
122 1
0.6%
120 1
0.6%
108 1
0.6%
76 1
0.6%

애인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.382716
Minimum0
Maximum863
Zeros70
Zeros (%)43.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T23:25:52.611291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37.75
95-th percentile69.9
Maximum863
Range863
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation109.56329
Coefficient of variation (CV)4.1528433
Kurtosis39.507589
Mean26.382716
Median Absolute Deviation (MAD)1
Skewness6.1736277
Sum4274
Variance12004.113
MonotonicityNot monotonic
2023-12-12T23:25:52.772162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 70
43.2%
2 14
 
8.6%
1 13
 
8.0%
3 9
 
5.6%
6 6
 
3.7%
4 5
 
3.1%
14 4
 
2.5%
7 3
 
1.9%
16 3
 
1.9%
9 3
 
1.9%
Other values (28) 32
19.8%
ValueCountFrequency (%)
0 70
43.2%
1 13
 
8.0%
2 14
 
8.6%
3 9
 
5.6%
4 5
 
3.1%
5 1
 
0.6%
6 6
 
3.7%
7 3
 
1.9%
8 1
 
0.6%
9 3
 
1.9%
ValueCountFrequency (%)
863 1
0.6%
744 1
0.6%
667 1
0.6%
427 1
0.6%
235 1
0.6%
125 1
0.6%
102 1
0.6%
76 1
0.6%
70 1
0.6%
68 1
0.6%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct129
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean610.37654
Minimum0
Maximum18868
Zeros3
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T23:25:52.951664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.05
Q133.75
median136.5
Q3342.25
95-th percentile2173.95
Maximum18868
Range18868
Interquartile range (IQR)308.5

Descriptive statistics

Standard deviation1882.9243
Coefficient of variation (CV)3.0848569
Kurtosis57.732412
Mean610.37654
Median Absolute Deviation (MAD)118
Skewness6.8289709
Sum98881
Variance3545404
MonotonicityNot monotonic
2023-12-12T23:25:53.163162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
172 3
 
1.9%
42 3
 
1.9%
16 3
 
1.9%
19 3
 
1.9%
8 3
 
1.9%
49 3
 
1.9%
0 3
 
1.9%
176 2
 
1.2%
1 2
 
1.2%
10 2
 
1.2%
Other values (119) 135
83.3%
ValueCountFrequency (%)
0 3
1.9%
1 2
1.2%
3 2
1.2%
5 2
1.2%
6 1
 
0.6%
8 3
1.9%
9 1
 
0.6%
10 2
1.2%
11 1
 
0.6%
12 1
 
0.6%
ValueCountFrequency (%)
18868 1
0.6%
8122 1
0.6%
7284 1
0.6%
6530 1
0.6%
5019 1
0.6%
4951 1
0.6%
4033 1
0.6%
3923 1
0.6%
2196 1
0.6%
1755 1
0.6%

미상
Real number (ℝ)

HIGH CORRELATION 

Distinct150
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1744.0864
Minimum0
Maximum64944
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T23:25:53.346657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q165.5
median324.5
Q3861.25
95-th percentile6033.25
Maximum64944
Range64944
Interquartile range (IQR)795.75

Descriptive statistics

Standard deviation6062.4123
Coefficient of variation (CV)3.4759816
Kurtosis76.251704
Mean1744.0864
Median Absolute Deviation (MAD)283
Skewness7.9892275
Sum282542
Variance36752843
MonotonicityNot monotonic
2023-12-12T23:25:53.539826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 3
 
1.9%
222 3
 
1.9%
39 2
 
1.2%
49 2
 
1.2%
10 2
 
1.2%
26 2
 
1.2%
53 2
 
1.2%
550 2
 
1.2%
9 2
 
1.2%
2 2
 
1.2%
Other values (140) 140
86.4%
ValueCountFrequency (%)
0 1
 
0.6%
1 1
 
0.6%
2 2
1.2%
3 1
 
0.6%
4 1
 
0.6%
7 1
 
0.6%
8 3
1.9%
9 2
1.2%
10 2
1.2%
15 1
 
0.6%
ValueCountFrequency (%)
64944 1
0.6%
25224 1
0.6%
24495 1
0.6%
18599 1
0.6%
9260 1
0.6%
8872 1
0.6%
8028 1
0.6%
7006 1
0.6%
6044 1
0.6%
5829 1
0.6%

Interactions

2023-12-12T23:25:47.529002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:35.808276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:36.889769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:38.377316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:39.501535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:40.732313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:41.653017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:42.875848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:44.270342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:45.456862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:46.511340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:47.620857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:35.911783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:37.338273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:38.465139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:39.604368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:40.809203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:41.769489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:42.965569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:44.376114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:45.533917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:46.623904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:47.707579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:36.020715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:37.433694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:38.574727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:39.721026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:40.886741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:41.914571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:43.060512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:44.485639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:45.612248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:46.707512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:47.812449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:36.130307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:37.569639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:38.687744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:39.867023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:40.974193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:42.005084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:43.165245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:44.601095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:45.709464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:46.805228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:47.906041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:36.222684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:37.664201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:38.801655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:39.991652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:41.058309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:42.111785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:43.271490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:44.717582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:45.818777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:46.902608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:47.982382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:36.303750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:37.771869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:38.893171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:40.127454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:41.129058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:42.199414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:43.352884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:44.811265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:45.900830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:46.982983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:48.060515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:36.397206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:37.888328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:38.992130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:40.237901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:41.212008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:42.330665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:43.765369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:44.934418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:45.995465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:47.073156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:48.139619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:36.469166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:37.970627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:39.071352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:40.324304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:41.302128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:42.444470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:43.844163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:45.040041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:46.070984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:47.172314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:48.239159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:36.559344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:38.064875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:39.163945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:40.421716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:41.392501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:42.561745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:43.961271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:45.159173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:46.158998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:47.269926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:48.329969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:36.641873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:38.156136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:39.242018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:40.519919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:41.467394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:42.669866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:44.076961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:45.268810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:46.255410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:47.360325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:48.426345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:36.767751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:38.267540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:39.368720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:40.627091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:41.556168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:42.781884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:44.168383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:45.368748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:46.384330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:47.447167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:25:53.665924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단독범학교동창교도소_소년원동료직장동료친인척군동료동네친구고향친구애인기타미상
단독범1.0000.9650.9200.8690.9700.9180.8630.7340.8590.9050.722
학교동창0.9651.0000.9810.9461.0000.9200.9370.9021.0000.8790.697
교도소_소년원동료0.9200.9811.0000.8710.9200.7250.8380.8721.0000.7850.634
직장동료0.8690.9460.8711.0000.9170.8590.9590.8250.9770.8720.766
친인척0.9701.0000.9200.9171.0000.9810.9490.8690.9500.9320.759
군동료0.9180.9200.7250.8590.9811.0000.8760.8680.8910.9050.683
동네친구0.8630.9370.8380.9590.9490.8761.0000.7680.9470.7870.915
고향친구0.7340.9020.8720.8250.8690.8680.7681.0000.8530.8640.506
애인0.8591.0001.0000.9770.9500.8910.9470.8531.0000.8130.755
기타0.9050.8790.7850.8720.9320.9050.7870.8640.8131.0000.674
미상0.7220.6970.6340.7660.7590.6830.9150.5060.7550.6741.000
2023-12-12T23:25:53.842428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단독범학교동창교도소_소년원동료직장동료친인척군동료동네친구고향친구애인기타미상
단독범1.0000.6730.4480.6420.6780.5120.6550.6030.5990.7150.689
학교동창0.6731.0000.5750.5950.6180.5290.7950.7720.7500.6250.506
교도소_소년원동료0.4480.5751.0000.4180.4760.4840.5720.5500.5620.4570.419
직장동료0.6420.5950.4181.0000.7220.5730.6170.6210.5840.8440.693
친인척0.6780.6180.4760.7221.0000.5150.6900.7060.6790.7810.655
군동료0.5120.5290.4840.5730.5151.0000.5060.5120.5390.6000.441
동네친구0.6550.7950.5720.6170.6900.5061.0000.8210.7750.6760.509
고향친구0.6030.7720.5500.6210.7060.5120.8211.0000.7230.6960.526
애인0.5990.7500.5620.5840.6790.5390.7750.7231.0000.6010.521
기타0.7150.6250.4570.8440.7810.6000.6760.6960.6011.0000.785
미상0.6890.5060.4190.6930.6550.4410.5090.5260.5210.7851.000

Missing values

2023-12-12T23:25:48.545321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:25:48.755779image/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
0절도7927060423161523111233898075486339233840
1장물2320702573221792017259173
2사기126953805219386418563440208157441886864944
3횡령24046259375635073585912514808872
4배임2849100638852131299635488
5손괴32130910318105127621686201855
6살인771305140951442123
7강도6884812753202455130192124
8방화11202104405846838
9성폭력3059127319922639147302681109
범죄분류단독범학교동창교도소_소년원동료직장동료친인척군동료동네친구고향친구애인기타미상
152폐기물관리법108722531001001265588
153풍속영업의규제에관한법률2052016202005420
154하천법223007202002229
155학교보건법690000000058
156학원의설립운영및과외교습에관한법률5132012300011316
157화물자동차운수사업법21970017581902228152
158화재로인한재해보상과보험가입에관한법률00000000000
159화재예방,소방시설설치유지및안전관리에관한법률2000000000019
160화학물질관리법3383708001322423300
161기타특별법37930508164130994661227665308028