Overview

Dataset statistics

Number of variables10
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory93.3 B

Variable types

Text1
Numeric9

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 시간별 범죄건수에 관한 통계임. (단위: 건)
Author대검찰청
URLhttps://www.data.go.kr/data/15085724/fileData.do

Alerts

00시00분-02시59분 is highly overall correlated with 03시00분-05시59분 and 7 other fieldsHigh correlation
03시00분-05시59분 is highly overall correlated with 00시00분-02시59분 and 7 other fieldsHigh correlation
06시00분-08시59분 is highly overall correlated with 00시00분-02시59분 and 7 other fieldsHigh correlation
09시00분-11시59분 is highly overall correlated with 00시00분-02시59분 and 7 other fieldsHigh correlation
12시00분-14시59분 is highly overall correlated with 00시00분-02시59분 and 7 other fieldsHigh correlation
15시00분-17시59분 is highly overall correlated with 00시00분-02시59분 and 7 other fieldsHigh correlation
18시00분-20시59분 is highly overall correlated with 00시00분-02시59분 and 7 other fieldsHigh correlation
21시00분-23시59분 is highly overall correlated with 00시00분-02시59분 and 7 other fieldsHigh correlation
미상 is highly overall correlated with 00시00분-02시59분 and 7 other fieldsHigh correlation
범죄분류 has unique valuesUnique
03시00분-05시59분 has unique valuesUnique
09시00분-11시59분 has unique valuesUnique
12시00분-14시59분 has unique valuesUnique
15시00분-17시59분 has unique valuesUnique
18시00분-20시59분 has unique valuesUnique
미상 has unique valuesUnique

Reproduction

Analysis started2023-12-13 00:32:32.389386
Analysis finished2023-12-13 00:32:38.293756
Duration5.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-13T09:32:38.434688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length16
Mean length8.8709677
Min length2

Characters and Unicode

Total characters275
Distinct characters97
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

Unique31 ?
Unique (%)100.0%

Sample

1st row절도
2nd row살인
3rd row강도
4th row방화
5th row성폭력
ValueCountFrequency (%)
절도 1
 
3.2%
가정폭력범죄의처벌등에관한특례법 1
 
3.2%
아동·청소년의성보호에관한법률(음란물등 1
 
3.2%
아동·청소년의성보호에관한법률(성매수등 1
 
3.2%
성매매알선등행위의처벌에관한법률 1
 
3.2%
마약류관리에관한법률(향정 1
 
3.2%
마약류관리에관한법률(마약 1
 
3.2%
마약류관리에관한법률(대마 1
 
3.2%
도로교통법(음주측정거부 1
 
3.2%
도로교통법(음주운전 1
 
3.2%
Other values (21) 21
67.7%
2023-12-13T09:32:38.747018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
법 15
 
5.5%
( 12
 
4.4%
) 12
 
4.4%
관 10
 
3.6%
도 9
 
3.3%
등 8
 
2.9%
에 7
 
2.5%
한 7
 
2.5%
률 6
 
2.2%
통 6
 
2.2%
Other values (87) 183
66.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 246
89.5%
Open Punctuation 12
 
4.4%
Close Punctuation 12
 
4.4%
Other Punctuation 5
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
법 15
 
6.1%
관 10
 
4.1%
도 9
 
3.7%
등 8
 
3.3%
에 7
 
2.8%
한 7
 
2.8%
률 6
 
2.4%
통 6
 
2.4%
교 6
 
2.4%
성 6
 
2.4%
Other values (84) 166
67.5%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Other Punctuation
ValueCountFrequency (%)
· 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 246
89.5%
Common 29
 
10.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
법 15
 
6.1%
관 10
 
4.1%
도 9
 
3.7%
등 8
 
3.3%
에 7
 
2.8%
한 7
 
2.8%
률 6
 
2.4%
통 6
 
2.4%
교 6
 
2.4%
성 6
 
2.4%
Other values (84) 166
67.5%
Common
ValueCountFrequency (%)
( 12
41.4%
) 12
41.4%
· 5
17.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 246
89.5%
ASCII 24
 
8.7%
None 5
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
법 15
 
6.1%
관 10
 
4.1%
도 9
 
3.7%
등 8
 
3.3%
에 7
 
2.8%
한 7
 
2.8%
률 6
 
2.4%
통 6
 
2.4%
교 6
 
2.4%
성 6
 
2.4%
Other values (84) 166
67.5%
ASCII
ValueCountFrequency (%)
( 12
50.0%
) 12
50.0%
None
ValueCountFrequency (%)
· 5
100.0%

00시00분-02시59분
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2724.9355
Minimum1
Maximum23460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T09:32:38.845838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q160.5
median272
Q31242
95-th percentile18047
Maximum23460
Range23459
Interquartile range (IQR)1181.5

Descriptive statistics

Standard deviation6063.1955
Coefficient of variation (CV)2.2250786
Kurtosis7.1885133
Mean2724.9355
Median Absolute Deviation (MAD)260
Skewness2.7990186
Sum84473
Variance36762340
MonotonicityNot monotonic
2023-12-13T09:32:38.929415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
12 2
 
6.5%
13751 1
 
3.2%
7 1
 
3.2%
358 1
 
3.2%
29 1
 
3.2%
587 1
 
3.2%
316 1
 
3.2%
1 1
 
3.2%
45 1
 
3.2%
506 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
1 1
3.2%
5 1
3.2%
7 1
3.2%
12 2
6.5%
29 1
3.2%
45 1
3.2%
54 1
3.2%
67 1
3.2%
82 1
3.2%
108 1
3.2%
ValueCountFrequency (%)
23460 1
3.2%
22343 1
3.2%
13751 1
3.2%
6052 1
3.2%
5928 1
3.2%
3200 1
3.2%
2621 1
3.2%
1473 1
3.2%
1011 1
3.2%
941 1
3.2%

03시00분-05시59분
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2473.9032
Minimum6
Maximum19137
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T09:32:39.282249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile10.5
Q170
median176
Q31183
95-th percentile16468.5
Maximum19137
Range19131
Interquartile range (IQR)1113

Descriptive statistics

Standard deviation5250.7892
Coefficient of variation (CV)2.1224716
Kurtosis5.5240589
Mean2473.9032
Median Absolute Deviation (MAD)165
Skewness2.538283
Sum76691
Variance27570787
MonotonicityNot monotonic
2023-12-13T09:32:39.377322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
14385 1
 
3.2%
69 1
 
3.2%
333 1
 
3.2%
14 1
 
3.2%
33 1
 
3.2%
389 1
 
3.2%
202 1
 
3.2%
6 1
 
3.2%
35 1
 
3.2%
372 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
6 1
3.2%
10 1
3.2%
11 1
3.2%
13 1
3.2%
14 1
3.2%
33 1
3.2%
35 1
3.2%
69 1
3.2%
71 1
3.2%
90 1
3.2%
ValueCountFrequency (%)
19137 1
3.2%
18552 1
3.2%
14385 1
3.2%
7054 1
3.2%
5170 1
3.2%
3296 1
3.2%
2735 1
3.2%
1489 1
3.2%
877 1
3.2%
777 1
3.2%

06시00분-08시59분
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2407.9355
Minimum4
Maximum21565
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T09:32:39.470711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11
Q153.5
median161
Q32172.5
95-th percentile11670.5
Maximum21565
Range21561
Interquartile range (IQR)2119

Descriptive statistics

Standard deviation4888.0947
Coefficient of variation (CV)2.0299941
Kurtosis7.6368204
Mean2407.9355
Median Absolute Deviation (MAD)149
Skewness2.6926979
Sum74646
Variance23893470
MonotonicityNot monotonic
2023-12-13T09:32:39.556009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
12 2
 
6.5%
138 2
 
6.5%
12502 1
 
3.2%
10 1
 
3.2%
507 1
 
3.2%
22 1
 
3.2%
120 1
 
3.2%
161 1
 
3.2%
16 1
 
3.2%
10839 1
 
3.2%
Other values (19) 19
61.3%
ValueCountFrequency (%)
4 1
3.2%
10 1
3.2%
12 2
6.5%
13 1
3.2%
16 1
3.2%
22 1
3.2%
49 1
3.2%
58 1
3.2%
75 1
3.2%
82 1
3.2%
ValueCountFrequency (%)
21565 1
3.2%
12502 1
3.2%
10839 1
3.2%
9738 1
3.2%
5437 1
3.2%
3555 1
3.2%
2995 1
3.2%
2615 1
3.2%
1730 1
3.2%
1108 1
3.2%

09시00분-11시59분
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3195.5484
Minimum4
Maximum26810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T09:32:39.639828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile24
Q1100
median397
Q32776
95-th percentile16553
Maximum26810
Range26806
Interquartile range (IQR)2676

Descriptive statistics

Standard deviation6369.2508
Coefficient of variation (CV)1.9931636
Kurtosis7.4008623
Mean3195.5484
Median Absolute Deviation (MAD)339
Skewness2.7321177
Sum99062
Variance40567356
MonotonicityNot monotonic
2023-12-13T09:32:39.731520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
21531 1
 
3.2%
94 1
 
3.2%
368 1
 
3.2%
41 1
 
3.2%
58 1
 
3.2%
447 1
 
3.2%
733 1
 
3.2%
275 1
 
3.2%
158 1
 
3.2%
84 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
4 1
3.2%
19 1
3.2%
29 1
3.2%
41 1
3.2%
58 1
3.2%
72 1
3.2%
84 1
3.2%
94 1
3.2%
106 1
3.2%
113 1
3.2%
ValueCountFrequency (%)
26810 1
3.2%
21531 1
3.2%
11575 1
3.2%
10444 1
3.2%
6759 1
3.2%
5052 1
3.2%
3895 1
3.2%
2993 1
3.2%
2559 1
3.2%
1857 1
3.2%

12시00분-14시59분
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3237.0323
Minimum7
Maximum29156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T09:32:39.820547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile24.5
Q190
median374
Q32707
95-th percentile18210.5
Maximum29156
Range29149
Interquartile range (IQR)2617

Descriptive statistics

Standard deviation6885.7699
Coefficient of variation (CV)2.1271861
Kurtosis8.7752172
Mean3237.0323
Median Absolute Deviation (MAD)298
Skewness2.9868753
Sum100348
Variance47413827
MonotonicityNot monotonic
2023-12-13T09:32:39.907682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
24623 1
 
3.2%
88 1
 
3.2%
502 1
 
3.2%
34 1
 
3.2%
76 1
 
3.2%
839 1
 
3.2%
544 1
 
3.2%
272 1
 
3.2%
82 1
 
3.2%
104 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
7 1
3.2%
19 1
3.2%
30 1
3.2%
34 1
3.2%
76 1
3.2%
77 1
3.2%
82 1
3.2%
88 1
3.2%
92 1
3.2%
104 1
3.2%
ValueCountFrequency (%)
29156 1
3.2%
24623 1
3.2%
11798 1
3.2%
7173 1
3.2%
6948 1
3.2%
4138 1
3.2%
3781 1
3.2%
2763 1
3.2%
2651 1
3.2%
1777 1
3.2%

15시00분-17시59분
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3880.0968
Minimum7
Maximum33739
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T09:32:39.996861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile31
Q1127.5
median553
Q33492.5
95-th percentile20590.5
Maximum33739
Range33732
Interquartile range (IQR)3365

Descriptive statistics

Standard deviation7882.5474
Coefficient of variation (CV)2.0315337
Kurtosis8.2034435
Mean3880.0968
Median Absolute Deviation (MAD)471
Skewness2.8644925
Sum120283
Variance62134554
MonotonicityNot monotonic
2023-12-13T09:32:40.089930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
26890 1
 
3.2%
100 1
 
3.2%
669 1
 
3.2%
37 1
 
3.2%
131 1
 
3.2%
1652 1
 
3.2%
618 1
 
3.2%
298 1
 
3.2%
124 1
 
3.2%
155 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
7 1
3.2%
25 1
3.2%
37 1
3.2%
41 1
3.2%
82 1
3.2%
90 1
3.2%
100 1
3.2%
124 1
3.2%
131 1
3.2%
149 1
3.2%
ValueCountFrequency (%)
33739 1
3.2%
26890 1
3.2%
14291 1
3.2%
10843 1
3.2%
7657 1
3.2%
5782 1
3.2%
4606 1
3.2%
3543 1
3.2%
3442 1
3.2%
1982 1
3.2%

18시00분-20시59분
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4439.1613
Minimum17
Maximum34814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T09:32:40.180614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q1120.5
median731
Q33379.5
95-th percentile24014.5
Maximum34814
Range34797
Interquartile range (IQR)3259

Descriptive statistics

Standard deviation8607.3263
Coefficient of variation (CV)1.9389533
Kurtosis5.6007032
Mean4439.1613
Median Absolute Deviation (MAD)676
Skewness2.4664291
Sum137614
Variance74086066
MonotonicityNot monotonic
2023-12-13T09:32:40.272491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
26654 1
 
3.2%
126 1
 
3.2%
859 1
 
3.2%
22 1
 
3.2%
115 1
 
3.2%
1914 1
 
3.2%
937 1
 
3.2%
55 1
 
3.2%
130 1
 
3.2%
299 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
17 1
3.2%
22 1
3.2%
30 1
3.2%
44 1
3.2%
55 1
3.2%
90 1
3.2%
100 1
3.2%
115 1
3.2%
126 1
3.2%
130 1
3.2%
ValueCountFrequency (%)
34814 1
3.2%
26654 1
3.2%
21375 1
3.2%
16269 1
3.2%
8828 1
3.2%
6178 1
3.2%
5876 1
3.2%
4335 1
3.2%
2424 1
3.2%
2415 1
3.2%

21시00분-23시59분
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6348.129
Minimum5
Maximum72175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T09:32:40.363449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile14
Q1126
median782
Q33947.5
95-th percentile30188.5
Maximum72175
Range72170
Interquartile range (IQR)3821.5

Descriptive statistics

Standard deviation14853
Coefficient of variation (CV)2.3397445
Kurtosis13.369822
Mean6348.129
Median Absolute Deviation (MAD)736
Skewness3.4876081
Sum196792
Variance2.2061161 × 108
MonotonicityNot monotonic
2023-12-13T09:32:40.477176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
14 2
 
6.5%
22167 1
 
3.2%
46 1
 
3.2%
960 1
 
3.2%
24 1
 
3.2%
93 1
 
3.2%
2692 1
 
3.2%
782 1
 
3.2%
139 1
 
3.2%
837 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
5 1
3.2%
14 2
6.5%
24 1
3.2%
46 1
3.2%
93 1
3.2%
95 1
3.2%
116 1
3.2%
136 1
3.2%
139 1
3.2%
141 1
3.2%
ValueCountFrequency (%)
72175 1
3.2%
38210 1
3.2%
22167 1
3.2%
21740 1
3.2%
10129 1
3.2%
7275 1
3.2%
5303 1
3.2%
5203 1
3.2%
2692 1
3.2%
2590 1
3.2%

미상
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3163.8065
Minimum17
Maximum21852
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T09:32:40.573489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile57
Q1221.5
median545
Q32493
95-th percentile19728.5
Maximum21852
Range21835
Interquartile range (IQR)2271.5

Descriptive statistics

Standard deviation6018.6809
Coefficient of variation (CV)1.9023543
Kurtosis5.6359917
Mean3163.8065
Median Absolute Deviation (MAD)476
Skewness2.5596983
Sum98078
Variance36224520
MonotonicityNot monotonic
2023-12-13T09:32:40.661155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
21852 1
 
3.2%
98 1
 
3.2%
255 1
 
3.2%
295 1
 
3.2%
188 1
 
3.2%
1775 1
 
3.2%
2505 1
 
3.2%
368 1
 
3.2%
362 1
 
3.2%
310 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
17 1
3.2%
45 1
3.2%
69 1
3.2%
98 1
3.2%
116 1
3.2%
163 1
3.2%
176 1
3.2%
188 1
3.2%
255 1
3.2%
289 1
3.2%
ValueCountFrequency (%)
21852 1
3.2%
21841 1
3.2%
17616 1
3.2%
6570 1
3.2%
5423 1
3.2%
4703 1
3.2%
3398 1
3.2%
2505 1
3.2%
2481 1
3.2%
1987 1
3.2%

Interactions

2023-12-13T09:32:37.558322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:32.658233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:33.200886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:33.877488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:34.429397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:35.212620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:35.783673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:36.409082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:36.979923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:37.611683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:32.711984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:33.273024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:33.932331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:34.492743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:35.273849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:35.839863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:36.465926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:37.040238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:37.675080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:32.774383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:33.341400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:33.997619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:34.569261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:35.341196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:35.911881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:36.536100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:37.108634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:37.740265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:32.830523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:33.411553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:34.057155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:34.634399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:35.403027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:35.979122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:36.593990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:37.169463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:37.810603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:32.885008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:33.484332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:34.119356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:34.906297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:35.462489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:36.057251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:36.655072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:37.232481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:37.879573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:32.948309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:33.566497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:34.182561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:34.965941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:35.528962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:36.131931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:36.719371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:37.295277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:37.942660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:33.017395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:33.650859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:34.245093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:35.029480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:35.592317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:36.199060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:36.787386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:37.363409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:38.002940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:33.080111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:33.738323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:34.311384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:35.091752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:35.658517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:36.268734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:36.854162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:37.431468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:38.065039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:33.146774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:33.819950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:34.376508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:35.158648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:35.724685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:36.346744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:36.921754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:32:37.499045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:32:40.726246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
범죄분류00시00분-02시59분03시00분-05시59분06시00분-08시59분09시00분-11시59분12시00분-14시59분15시00분-17시59분18시00분-20시59분21시00분-23시59분미상
범죄분류1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
00시00분-02시59분1.0001.0001.0000.8460.8680.8650.8750.9220.9550.800
03시00분-05시59분1.0001.0001.0000.9740.8940.9820.8950.9330.8680.965
06시00분-08시59분1.0000.8460.9741.0000.9380.9840.9511.0000.8860.965
09시00분-11시59분1.0000.8680.8940.9381.0000.9721.0000.9920.8890.782
12시00분-14시59분1.0000.8650.9820.9840.9721.0000.9620.9540.8950.953
15시00분-17시59분1.0000.8750.8950.9511.0000.9621.0000.9940.8740.784
18시00분-20시59분1.0000.9220.9331.0000.9920.9540.9941.0000.9520.848
21시00분-23시59분1.0000.9550.8680.8860.8890.8950.8740.9521.0000.834
미상1.0000.8000.9650.9650.7820.9530.7840.8480.8341.000
2023-12-13T09:32:40.822477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
00시00분-02시59분03시00분-05시59분06시00분-08시59분09시00분-11시59분12시00분-14시59분15시00분-17시59분18시00분-20시59분21시00분-23시59분미상
00시00분-02시59분1.0000.9940.9230.8540.8800.8860.9470.9800.819
03시00분-05시59분0.9941.0000.9290.8500.8790.8830.9380.9730.812
06시00분-08시59분0.9230.9291.0000.9310.9510.9460.9490.9230.810
09시00분-11시59분0.8540.8500.9311.0000.9850.9760.9450.8820.897
12시00분-14시59분0.8800.8790.9510.9851.0000.9890.9600.9110.881
15시00분-17시59분0.8860.8830.9460.9760.9891.0000.9710.9180.885
18시00분-20시59분0.9470.9380.9490.9450.9600.9711.0000.9760.883
21시00분-23시59분0.9800.9730.9230.8820.9110.9180.9761.0000.850
미상0.8190.8120.8100.8970.8810.8850.8830.8501.000

Missing values

2023-12-13T09:32:38.142344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:32:38.249992image/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

범죄분류00시00분-02시59분03시00분-05시59분06시00분-08시59분09시00분-11시59분12시00분-14시59분15시00분-17시59분18시00분-20시59분21시00분-23시59분미상
0절도137511438512502215312462326890266542216721852
1살인676975948810012614198
2강도13216258727790100136163
3방화15714082113156149197248116
4성폭력320032962615255926513442433553035423
5폭행22343185529738115751179814291213753821017616
6상해6052517029953895378146066178101296570
7협박10117771108185717771982242425902481
8공갈1871331034863655537336731748
9약취와유인12111329304130545
범죄분류00시00분-02시59분03시00분-05시59분06시00분-08시59분09시00분-11시59분12시00분-14시59분15시00분-17시59분18시00분-20시59분21시00분-23시59분미상
21도로교통법(사고후미조치)262127355437675969487657882872751987
22도로교통법(음주운전)234601913710839505241385782162697217521841
23도로교통법(음주측정거부)50637213884104155299837310
24마약류관리에관한법률(대마)45351215882124130139362
25마약류관리에관한법률(마약)16162752722985514368
26마약류관리에관한법률(향정)3162021617335446189377822505
27성매매알선등행위의처벌에관한법률5873891204478391652191426921775
28아동·청소년의성보호에관한법률(성매수등)293322587613111593188
29아동·청소년의성보호에관한법률(음란물등)1214124134372224295
30특가법(도주차량)358333507368502669859960255