Overview

Dataset statistics

Number of variables7
Number of observations205
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.5 KiB
Average record size in memory62.6 B

Variable types

Text1
Numeric6

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 범죄의 발생 검거상황(총괄)에 관한 통계임.
Author대검찰청
URLhttps://www.data.go.kr/data/15084748/fileData.do

Alerts

발생건수(건) is highly overall correlated with 검거건수(건) and 3 other fieldsHigh correlation
검거건수(건) is highly overall correlated with 발생건수(건) and 3 other fieldsHigh correlation
남자검거인원(명) is highly overall correlated with 발생건수(건) and 3 other fieldsHigh correlation
여자검거인원(명) is highly overall correlated with 발생건수(건) and 4 other fieldsHigh correlation
미상검거인원(명) is highly overall correlated with 발생건수(건) and 4 other fieldsHigh correlation
법인(개) is highly overall correlated with 여자검거인원(명) and 1 other fieldsHigh correlation
범죄분류 has unique valuesUnique
여자검거인원(명) has 12 (5.9%) zerosZeros
미상검거인원(명) has 36 (17.6%) zerosZeros
법인(개) has 66 (32.2%) zerosZeros

Reproduction

Analysis started2023-12-12 16:26:31.790654
Analysis finished2023-12-12 16:26:35.725073
Duration3.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct205
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-13T01:26:35.897086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length24
Mean length8.2439024
Min length2

Characters and Unicode

Total characters1690
Distinct characters244
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

Unique205 ?
Unique (%)100.0%

Sample

1st row절도
2nd row불법사용
3rd row침입절도
4th row장물
5th row사기
ValueCountFrequency (%)
관한법률 29
 
8.6%
및 22
 
6.5%
관리에 4
 
1.2%
마약류관리에 3
 
0.9%
보호에 3
 
0.9%
관한 2
 
0.6%
규제에 2
 
0.6%
규제 2
 
0.6%
아동,청소년의 2
 
0.6%
등 2
 
0.6%
Other values (260) 267
79.0%
2023-12-13T01:26:36.305377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
133
 
7.9%
법 117
 
6.9%
관 68
 
4.0%
한 42
 
2.5%
에 41
 
2.4%
률 34
 
2.0%
리 31
 
1.8%
의 29
 
1.7%
보 27
 
1.6%
사 26
 
1.5%
Other values (234) 1142
67.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1507
89.2%
Space Separator 133
 
7.9%
Other Punctuation 20
 
1.2%
Close Punctuation 15
 
0.9%
Open Punctuation 15
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
법 117
 
7.8%
관 68
 
4.5%
한 42
 
2.8%
에 41
 
2.7%
률 34
 
2.3%
리 31
 
2.1%
의 29
 
1.9%
보 27
 
1.8%
사 26
 
1.7%
등 25
 
1.7%
Other values (228) 1067
70.8%
Other Punctuation
ValueCountFrequency (%)
, 12
60.0%
· 5
25.0%
/ 3
 
15.0%
Space Separator
ValueCountFrequency (%)
133
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1507
89.2%
Common 183
 
10.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
법 117
 
7.8%
관 68
 
4.5%
한 42
 
2.8%
에 41
 
2.7%
률 34
 
2.3%
리 31
 
2.1%
의 29
 
1.9%
보 27
 
1.8%
사 26
 
1.7%
등 25
 
1.7%
Other values (228) 1067
70.8%
Common
ValueCountFrequency (%)
133
72.7%
) 15
 
8.2%
( 15
 
8.2%
, 12
 
6.6%
· 5
 
2.7%
/ 3
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1507
89.2%
ASCII 178
 
10.5%
None 5
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
133
74.7%
) 15
 
8.4%
( 15
 
8.4%
, 12
 
6.7%
/ 3
 
1.7%
Hangul
ValueCountFrequency (%)
법 117
 
7.8%
관 68
 
4.5%
한 42
 
2.8%
에 41
 
2.7%
률 34
 
2.3%
리 31
 
2.1%
의 29
 
1.9%
보 27
 
1.8%
사 26
 
1.7%
등 25
 
1.7%
Other values (228) 1067
70.8%
None
ValueCountFrequency (%)
· 5
100.0%

발생건수(건)
Real number (ℝ)

HIGH CORRELATION 

Distinct195
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8901.8341
Minimum0
Maximum233203
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T01:26:36.437799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.8
Q1223
median886
Q33358
95-th percentile46534.2
Maximum233203
Range233203
Interquartile range (IQR)3135

Descriptive statistics

Standard deviation30297.941
Coefficient of variation (CV)3.4035616
Kurtosis31.303025
Mean8901.8341
Median Absolute Deviation (MAD)830
Skewness5.4509911
Sum1824876
Variance9.1796523 × 108
MonotonicityNot monotonic
2023-12-13T01:26:36.583070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3
 
1.5%
213 3
 
1.5%
1087 2
 
1.0%
3 2
 
1.0%
19 2
 
1.0%
287 2
 
1.0%
223 2
 
1.0%
7 2
 
1.0%
1019 1
 
0.5%
1946 1
 
0.5%
Other values (185) 185
90.2%
ValueCountFrequency (%)
0 1
 
0.5%
1 1
 
0.5%
2 3
1.5%
3 2
1.0%
7 2
1.0%
12 1
 
0.5%
15 1
 
0.5%
19 2
1.0%
20 1
 
0.5%
21 1
 
0.5%
ValueCountFrequency (%)
233203 1
0.5%
185509 1
0.5%
178693 1
0.5%
176994 1
0.5%
163858 1
0.5%
56556 1
0.5%
52610 1
0.5%
50270 1
0.5%
50247 1
0.5%
49311 1
0.5%

검거건수(건)
Real number (ℝ)

HIGH CORRELATION 

Distinct194
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7594.9415
Minimum0
Maximum185556
Zeros2
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T01:26:36.749355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q1210
median801
Q32866
95-th percentile31252
Maximum185556
Range185556
Interquartile range (IQR)2656

Descriptive statistics

Standard deviation26156.046
Coefficient of variation (CV)3.4438772
Kurtosis33.066683
Mean7594.9415
Median Absolute Deviation (MAD)738
Skewness5.6224856
Sum1556963
Variance6.8413873 × 108
MonotonicityNot monotonic
2023-12-13T01:26:36.960660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3
 
1.5%
210 2
 
1.0%
0 2
 
1.0%
1532 2
 
1.0%
2139 2
 
1.0%
17 2
 
1.0%
196 2
 
1.0%
26 2
 
1.0%
45 2
 
1.0%
286 2
 
1.0%
Other values (184) 184
89.8%
ValueCountFrequency (%)
0 2
1.0%
1 1
 
0.5%
2 3
1.5%
3 1
 
0.5%
4 1
 
0.5%
5 1
 
0.5%
7 1
 
0.5%
10 1
 
0.5%
15 1
 
0.5%
17 2
1.0%
ValueCountFrequency (%)
185556 1
0.5%
181051 1
0.5%
178069 1
0.5%
155857 1
0.5%
107802 1
0.5%
48572 1
0.5%
47967 1
0.5%
46936 1
0.5%
41835 1
0.5%
37563 1
0.5%

남자검거인원(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct193
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7244.9366
Minimum0
Maximum186274
Zeros2
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T01:26:37.190917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.2
Q1222
median849
Q32639
95-th percentile29820
Maximum186274
Range186274
Interquartile range (IQR)2417

Descriptive statistics

Standard deviation24378.733
Coefficient of variation (CV)3.364934
Kurtosis34.600349
Mean7244.9366
Median Absolute Deviation (MAD)797
Skewness5.7036907
Sum1485212
Variance5.9432263 × 108
MonotonicityNot monotonic
2023-12-13T01:26:37.378826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 3
 
1.5%
7 3
 
1.5%
537 2
 
1.0%
2 2
 
1.0%
184 2
 
1.0%
731 2
 
1.0%
521 2
 
1.0%
1 2
 
1.0%
1132 2
 
1.0%
0 2
 
1.0%
Other values (183) 183
89.3%
ValueCountFrequency (%)
0 2
1.0%
1 2
1.0%
2 2
1.0%
3 1
 
0.5%
7 3
1.5%
11 1
 
0.5%
17 1
 
0.5%
18 1
 
0.5%
19 1
 
0.5%
20 1
 
0.5%
ValueCountFrequency (%)
186274 1
0.5%
165912 1
0.5%
160701 1
0.5%
146924 1
0.5%
77177 1
0.5%
61452 1
0.5%
45035 1
0.5%
36967 1
0.5%
35739 1
0.5%
34958 1
0.5%

여자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct159
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1708.5415
Minimum0
Maximum46783
Zeros12
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T01:26:37.553957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median147
Q3704
95-th percentile6376.4
Maximum46783
Range46783
Interquartile range (IQR)673

Descriptive statistics

Standard deviation5809.0455
Coefficient of variation (CV)3.4000026
Kurtosis37.574068
Mean1708.5415
Median Absolute Deviation (MAD)142
Skewness5.8638625
Sum350251
Variance33745010
MonotonicityNot monotonic
2023-12-13T01:26:37.752930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12
 
5.9%
3 5
 
2.4%
22 3
 
1.5%
11 3
 
1.5%
14 3
 
1.5%
4 3
 
1.5%
47 3
 
1.5%
326 2
 
1.0%
77 2
 
1.0%
151 2
 
1.0%
Other values (149) 167
81.5%
ValueCountFrequency (%)
0 12
5.9%
1 2
 
1.0%
2 2
 
1.0%
3 5
2.4%
4 3
 
1.5%
5 1
 
0.5%
6 1
 
0.5%
8 1
 
0.5%
9 2
 
1.0%
11 3
 
1.5%
ValueCountFrequency (%)
46783 1
0.5%
41901 1
0.5%
39144 1
0.5%
25024 1
0.5%
19450 1
0.5%
16317 1
0.5%
12267 1
0.5%
8480 1
0.5%
8339 1
0.5%
7862 1
0.5%

미상검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.45366
Minimum0
Maximum3585
Zeros36
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T01:26:37.906712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median13
Q349
95-th percentile598.6
Maximum3585
Range3585
Interquartile range (IQR)47

Descriptive statistics

Standard deviation441.69122
Coefficient of variation (CV)3.4385258
Kurtosis38.114312
Mean128.45366
Median Absolute Deviation (MAD)13
Skewness5.8577329
Sum26333
Variance195091.13
MonotonicityNot monotonic
2023-12-13T01:26:38.060904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
 
17.6%
2 13
 
6.3%
3 9
 
4.4%
4 8
 
3.9%
1 6
 
2.9%
16 5
 
2.4%
9 5
 
2.4%
7 4
 
2.0%
11 4
 
2.0%
5 4
 
2.0%
Other values (81) 111
54.1%
ValueCountFrequency (%)
0 36
17.6%
1 6
 
2.9%
2 13
 
6.3%
3 9
 
4.4%
4 8
 
3.9%
5 4
 
2.0%
6 3
 
1.5%
7 4
 
2.0%
8 2
 
1.0%
9 5
 
2.4%
ValueCountFrequency (%)
3585 1
0.5%
3414 1
0.5%
2574 1
0.5%
1885 1
0.5%
1453 1
0.5%
1126 1
0.5%
1123 1
0.5%
879 1
0.5%
723 1
0.5%
705 1
0.5%

법인(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.34146
Minimum0
Maximum6345
Zeros66
Zeros (%)32.2%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T01:26:38.226566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q360
95-th percentile480.6
Maximum6345
Range6345
Interquartile range (IQR)60

Descriptive statistics

Standard deviation517.31107
Coefficient of variation (CV)4.4464893
Kurtosis107.55247
Mean116.34146
Median Absolute Deviation (MAD)5
Skewness9.6016906
Sum23850
Variance267610.75
MonotonicityNot monotonic
2023-12-13T01:26:38.408301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 66
32.2%
1 14
 
6.8%
2 8
 
3.9%
4 6
 
2.9%
3 6
 
2.9%
11 5
 
2.4%
12 4
 
2.0%
6 4
 
2.0%
5 4
 
2.0%
134 3
 
1.5%
Other values (68) 85
41.5%
ValueCountFrequency (%)
0 66
32.2%
1 14
 
6.8%
2 8
 
3.9%
3 6
 
2.9%
4 6
 
2.9%
5 4
 
2.0%
6 4
 
2.0%
7 3
 
1.5%
8 2
 
1.0%
9 1
 
0.5%
ValueCountFrequency (%)
6345 1
0.5%
2856 1
0.5%
1442 1
0.5%
1346 1
0.5%
1231 1
0.5%
842 1
0.5%
669 1
0.5%
577 1
0.5%
538 1
0.5%
526 1
0.5%

Interactions

2023-12-13T01:26:34.931914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:32.087439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:32.582880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:33.334859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:33.890660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:34.370768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:35.012282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:32.149472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:32.651342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:33.407394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:33.962346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:34.457791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:35.097327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:32.230015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:32.728187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:33.491275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:34.045383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:34.555298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:35.199870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:32.340072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:32.818001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:33.572882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:34.124692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:34.642915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:35.295022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:32.439456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:32.886001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:33.668358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:34.197021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:34.732800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:35.396051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:32.512431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:33.241862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:33.797530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:34.281789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:26:34.835851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:26:38.520289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생건수(건)검거건수(건)남자검거인원(명)여자검거인원(명)미상검거인원(명)법인(개)
발생건수(건)1.0000.8170.8630.8770.7230.695
검거건수(건)0.8171.0000.9660.8890.8050.328
남자검거인원(명)0.8630.9661.0000.9810.8760.283
여자검거인원(명)0.8770.8890.9811.0000.9160.724
미상검거인원(명)0.7230.8050.8760.9161.0000.779
법인(개)0.6950.3280.2830.7240.7791.000
2023-12-13T01:26:38.905161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생건수(건)검거건수(건)남자검거인원(명)여자검거인원(명)미상검거인원(명)법인(개)
발생건수(건)1.0000.9940.9610.8480.6430.472
검거건수(건)0.9941.0000.9650.8510.6250.484
남자검거인원(명)0.9610.9651.0000.8640.6760.479
여자검거인원(명)0.8480.8510.8641.0000.6990.536
미상검거인원(명)0.6430.6250.6760.6991.0000.612
법인(개)0.4720.4840.4790.5360.6121.000

Missing values

2023-12-13T01:26:35.534749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:26:35.663036image/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

범죄분류발생건수(건)검거건수(건)남자검거인원(명)여자검거인원(명)미상검거인원(명)법인(개)
0절도176994107802771772502446712
1불법사용5484506452900
2침입절도68136011309521630
3장물193419102639481111
4사기233203185556165912467833585842
5컴퓨터등사용사기408517031651412266
6부당이득5934501951
7편의시설부정이용7135305371161422
8전기통신금융사기피해금환급에관한특별법23481239992380132
9보험사기방지특별법12341198183978620
범죄분류발생건수(건)검거건수(건)남자검거인원(명)여자검거인원(명)미상검거인원(명)법인(개)
195폐기물관리법16381539181415153538
196풍속영업의 규제에 관한법률2052042247700
197하천법21321023052310
198학교보건법8280523000
199학원의 설립운영 및 과외교습에 관한법률51151018737300
200화물자동차 운수사업법2110208626531071281
201화재로 인한 재해보상과 보험가입에 관한법률1500000
202화재예방·소방시설설치유지 및 안전관리에 관한법률2527261308
203화학물질관리법6796836961444265
204기타특별법5027048572369671631718852856