Overview

Dataset statistics

Number of variables10
Number of observations162
Missing cells9
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.2 KiB
Average record size in memory89.8 B

Variable types

Text1
Numeric9

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 범죄발생부터 검거까지의 기간에 따른 범죄 통계임. (단위: 건)
Author대검찰청
URLhttps://www.data.go.kr/data/15085620/fileData.do

Alerts

1일 이내 is highly overall correlated with 2일 이내 and 6 other fieldsHigh correlation
2일 이내 is highly overall correlated with 1일 이내 and 6 other fieldsHigh correlation
3일 이내 is highly overall correlated with 1일 이내 and 6 other fieldsHigh correlation
10일 이내 is highly overall correlated with 1일 이내 and 6 other fieldsHigh correlation
1개월 이내 is highly overall correlated with 1일 이내 and 7 other fieldsHigh correlation
3개월 이내 is highly overall correlated with 1일 이내 and 7 other fieldsHigh correlation
6개월 이내 is highly overall correlated with 1일 이내 and 7 other fieldsHigh correlation
1년 이내 is highly overall correlated with 1일 이내 and 7 other fieldsHigh correlation
1년 초과 is highly overall correlated with 1개월 이내 and 3 other fieldsHigh correlation
범죄분류 has unique valuesUnique
1일 이내 has 5 (3.1%) zerosZeros
2일 이내 has 22 (13.6%) zerosZeros
3일 이내 has 31 (19.1%) zerosZeros
10일 이내 has 7 (4.3%) zerosZeros
1개월 이내 has 2 (1.2%) zerosZeros
3개월 이내 has 3 (1.9%) zerosZeros
1년 이내 has 2 (1.2%) zerosZeros
1년 초과 has 3 (1.9%) zerosZeros

Reproduction

Analysis started2023-12-12 11:32:52.149182
Analysis finished2023-12-12 11:33:06.499787
Duration14.35 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
2023-12-12T20:33:06.782485image/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%
2023-12-12T20:33:07.483068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
법 114
 
8.8%
관 67
 
5.1%
한 41
 
3.1%
에 40
 
3.1%
률 34
 
2.6%
리 31
 
2.4%
의 27
 
2.1%
보 26
 
2.0%
및 23
 
1.8%
업 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 (%)
법 114
 
9.0%
관 67
 
5.3%
한 41
 
3.2%
에 40
 
3.2%
률 34
 
2.7%
리 31
 
2.4%
의 27
 
2.1%
보 26
 
2.1%
및 23
 
1.8%
업 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 (%)
법 114
 
9.0%
관 67
 
5.3%
한 41
 
3.2%
에 40
 
3.2%
률 34
 
2.7%
리 31
 
2.4%
의 27
 
2.1%
보 26
 
2.1%
및 23
 
1.8%
업 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 (%)
법 114
 
9.0%
관 67
 
5.3%
한 41
 
3.2%
에 40
 
3.2%
률 34
 
2.7%
리 31
 
2.4%
의 27
 
2.1%
보 26
 
2.1%
및 23
 
1.8%
업 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%

1일 이내
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct125
Distinct (%)77.6%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean3461.4348
Minimum0
Maximum132881
Zeros5
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T20:33:07.757154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q115
median106
Q3620
95-th percentile12232
Maximum132881
Range132881
Interquartile range (IQR)605

Descriptive statistics

Standard deviation15147.652
Coefficient of variation (CV)4.3761194
Kurtosis49.005559
Mean3461.4348
Median Absolute Deviation (MAD)102
Skewness6.7697211
Sum557291
Variance2.2945136 × 108
MonotonicityNot monotonic
2023-12-12T20:33:08.056768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
3.1%
1 5
 
3.1%
24 4
 
2.5%
11 4
 
2.5%
4 4
 
2.5%
12 3
 
1.9%
3 3
 
1.9%
10 3
 
1.9%
5 3
 
1.9%
15 3
 
1.9%
Other values (115) 124
76.5%
ValueCountFrequency (%)
0 5
3.1%
1 5
3.1%
2 1
 
0.6%
3 3
1.9%
4 4
2.5%
5 3
1.9%
6 1
 
0.6%
7 2
 
1.2%
8 1
 
0.6%
9 1
 
0.6%
ValueCountFrequency (%)
132881 1
0.6%
103617 1
0.6%
82983 1
0.6%
34757 1
0.6%
24950 1
0.6%
17118 1
0.6%
13548 1
0.6%
12540 1
0.6%
12232 1
0.6%
11952 1
0.6%

2일 이내
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)39.1%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean190.29193
Minimum0
Maximum6360
Zeros22
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T20:33:08.330853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q332
95-th percentile555
Maximum6360
Range6360
Interquartile range (IQR)31

Descriptive statistics

Standard deviation794.16235
Coefficient of variation (CV)4.1733896
Kurtosis37.893045
Mean190.29193
Median Absolute Deviation (MAD)7
Skewness5.9672442
Sum30637
Variance630693.83
MonotonicityNot monotonic
2023-12-12T20:33:08.598071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 25
 
15.4%
0 22
 
13.6%
2 11
 
6.8%
3 11
 
6.8%
9 8
 
4.9%
7 5
 
3.1%
6 4
 
2.5%
10 4
 
2.5%
20 3
 
1.9%
14 3
 
1.9%
Other values (53) 65
40.1%
ValueCountFrequency (%)
0 22
13.6%
1 25
15.4%
2 11
6.8%
3 11
6.8%
4 2
 
1.2%
5 3
 
1.9%
6 4
 
2.5%
7 5
 
3.1%
8 1
 
0.6%
9 8
 
4.9%
ValueCountFrequency (%)
6360 1
0.6%
5429 1
0.6%
3784 1
0.6%
3459 1
0.6%
2442 1
0.6%
1115 1
0.6%
964 1
0.6%
889 1
0.6%
555 1
0.6%
451 1
0.6%

3일 이내
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)39.8%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean220.79503
Minimum0
Maximum7162
Zeros31
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T20:33:08.890298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q331
95-th percentile814
Maximum7162
Range7162
Interquartile range (IQR)30

Descriptive statistics

Standard deviation928.08288
Coefficient of variation (CV)4.2033685
Kurtosis36.636028
Mean220.79503
Median Absolute Deviation (MAD)7
Skewness5.9155987
Sum35548
Variance861337.83
MonotonicityNot monotonic
2023-12-12T20:33:09.162182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31
19.1%
1 17
 
10.5%
2 10
 
6.2%
3 9
 
5.6%
10 6
 
3.7%
6 6
 
3.7%
5 5
 
3.1%
11 4
 
2.5%
31 4
 
2.5%
7 4
 
2.5%
Other values (54) 65
40.1%
ValueCountFrequency (%)
0 31
19.1%
1 17
10.5%
2 10
 
6.2%
3 9
 
5.6%
4 2
 
1.2%
5 5
 
3.1%
6 6
 
3.7%
7 4
 
2.5%
8 2
 
1.2%
9 2
 
1.2%
ValueCountFrequency (%)
7162 1
0.6%
6160 1
0.6%
5582 1
0.6%
3521 1
0.6%
2418 1
0.6%
1545 1
0.6%
1151 1
0.6%
1029 1
0.6%
814 1
0.6%
456 1
0.6%

10일 이내
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct108
Distinct (%)67.1%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1040.0559
Minimum0
Maximum29666
Zeros7
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T20:33:09.431600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q112
median53
Q3209
95-th percentile4729
Maximum29666
Range29666
Interquartile range (IQR)197

Descriptive statistics

Standard deviation3888.4862
Coefficient of variation (CV)3.738728
Kurtosis32.851734
Mean1040.0559
Median Absolute Deviation (MAD)50
Skewness5.5637602
Sum167449
Variance15120325
MonotonicityNot monotonic
2023-12-12T20:33:09.694932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
4.3%
2 7
 
4.3%
3 4
 
2.5%
8 4
 
2.5%
5 4
 
2.5%
12 4
 
2.5%
18 3
 
1.9%
20 3
 
1.9%
14 3
 
1.9%
10 3
 
1.9%
Other values (98) 119
73.5%
ValueCountFrequency (%)
0 7
4.3%
1 2
 
1.2%
2 7
4.3%
3 4
2.5%
4 1
 
0.6%
5 4
2.5%
6 2
 
1.2%
7 3
1.9%
8 4
2.5%
10 3
1.9%
ValueCountFrequency (%)
29666 1
0.6%
25096 1
0.6%
19927 1
0.6%
19822 1
0.6%
8624 1
0.6%
8276 1
0.6%
5558 1
0.6%
4880 1
0.6%
4729 1
0.6%
4073 1
0.6%

1개월 이내
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct134
Distinct (%)83.2%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1347.0932
Minimum0
Maximum29668
Zeros2
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T20:33:09.936974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q132
median130
Q3567
95-th percentile5758
Maximum29668
Range29668
Interquartile range (IQR)535

Descriptive statistics

Standard deviation4223.67
Coefficient of variation (CV)3.1353956
Kurtosis26.525269
Mean1347.0932
Median Absolute Deviation (MAD)116
Skewness5.0096497
Sum216882
Variance17839388
MonotonicityNot monotonic
2023-12-12T20:33:10.203737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 5
 
3.1%
6 3
 
1.9%
17 3
 
1.9%
5 3
 
1.9%
3 2
 
1.2%
348 2
 
1.2%
96 2
 
1.2%
14 2
 
1.2%
69 2
 
1.2%
90 2
 
1.2%
Other values (124) 135
83.3%
ValueCountFrequency (%)
0 2
1.2%
1 1
 
0.6%
2 1
 
0.6%
3 2
1.2%
4 1
 
0.6%
5 3
1.9%
6 3
1.9%
7 1
 
0.6%
8 1
 
0.6%
10 2
1.2%
ValueCountFrequency (%)
29668 1
0.6%
24894 1
0.6%
23635 1
0.6%
23308 1
0.6%
11155 1
0.6%
10682 1
0.6%
7910 1
0.6%
6793 1
0.6%
5758 1
0.6%
5498 1
0.6%

3개월 이내
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct139
Distinct (%)86.3%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1265.8075
Minimum0
Maximum45997
Zeros3
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T20:33:10.451154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q152
median192
Q3729
95-th percentile5220
Maximum45997
Range45997
Interquartile range (IQR)677

Descriptive statistics

Standard deviation4207.9637
Coefficient of variation (CV)3.3243316
Kurtosis82.339425
Mean1265.8075
Median Absolute Deviation (MAD)171
Skewness8.288846
Sum203795
Variance17706959
MonotonicityNot monotonic
2023-12-12T20:33:10.699587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
1.9%
27 3
 
1.9%
135 3
 
1.9%
84 2
 
1.2%
41 2
 
1.2%
20 2
 
1.2%
551 2
 
1.2%
69 2
 
1.2%
120 2
 
1.2%
18 2
 
1.2%
Other values (129) 138
85.2%
ValueCountFrequency (%)
0 3
1.9%
2 2
1.2%
3 1
 
0.6%
4 1
 
0.6%
5 1
 
0.6%
7 1
 
0.6%
9 1
 
0.6%
11 1
 
0.6%
13 1
 
0.6%
14 1
 
0.6%
ValueCountFrequency (%)
45997 1
0.6%
19153 1
0.6%
11510 1
0.6%
8883 1
0.6%
8792 1
0.6%
6902 1
0.6%
6044 1
0.6%
6011 1
0.6%
5220 1
0.6%
4798 1
0.6%

6개월 이내
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)80.7%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean635.45963
Minimum0
Maximum26365
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T20:33:10.923383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q138
median100
Q3379
95-th percentile2028
Maximum26365
Range26365
Interquartile range (IQR)341

Descriptive statistics

Standard deviation2337.1354
Coefficient of variation (CV)3.6778661
Kurtosis94.759792
Mean635.45963
Median Absolute Deviation (MAD)88
Skewness9.0726906
Sum102309
Variance5462201.9
MonotonicityNot monotonic
2023-12-12T20:33:11.141505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 5
 
3.1%
3 5
 
3.1%
22 4
 
2.5%
1 3
 
1.9%
57 3
 
1.9%
140 2
 
1.2%
64 2
 
1.2%
54 2
 
1.2%
42 2
 
1.2%
23 2
 
1.2%
Other values (120) 131
80.9%
ValueCountFrequency (%)
0 1
 
0.6%
1 3
1.9%
2 5
3.1%
3 5
3.1%
5 2
 
1.2%
9 2
 
1.2%
10 1
 
0.6%
11 1
 
0.6%
12 1
 
0.6%
15 1
 
0.6%
ValueCountFrequency (%)
26365 1
0.6%
10741 1
0.6%
5781 1
0.6%
4332 1
0.6%
3848 1
0.6%
3063 1
0.6%
2777 1
0.6%
2077 1
0.6%
2028 1
0.6%
1843 1
0.6%

1년 이내
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct125
Distinct (%)77.6%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean513.97516
Minimum0
Maximum23429
Zeros2
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T20:33:11.352557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q125
median86
Q3253
95-th percentile1717
Maximum23429
Range23429
Interquartile range (IQR)228

Descriptive statistics

Standard deviation2034.6045
Coefficient of variation (CV)3.9585658
Kurtosis103.19913
Mean513.97516
Median Absolute Deviation (MAD)73
Skewness9.5355732
Sum82750
Variance4139615.4
MonotonicityNot monotonic
2023-12-12T20:33:12.136370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 4
 
2.5%
1 4
 
2.5%
96 3
 
1.9%
16 3
 
1.9%
40 3
 
1.9%
12 3
 
1.9%
65 3
 
1.9%
28 3
 
1.9%
11 2
 
1.2%
26 2
 
1.2%
Other values (115) 131
80.9%
ValueCountFrequency (%)
0 2
1.2%
1 4
2.5%
2 4
2.5%
3 2
1.2%
4 1
 
0.6%
5 1
 
0.6%
6 1
 
0.6%
7 1
 
0.6%
8 2
1.2%
10 1
 
0.6%
ValueCountFrequency (%)
23429 1
0.6%
8716 1
0.6%
4333 1
0.6%
3288 1
0.6%
3117 1
0.6%
2590 1
0.6%
2074 1
0.6%
1960 1
0.6%
1717 1
0.6%
1665 1
0.6%

1년 초과
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct132
Distinct (%)82.0%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean995.6646
Minimum0
Maximum54240
Zeros3
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T20:33:12.387987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q136
median134
Q3439
95-th percentile3850
Maximum54240
Range54240
Interquartile range (IQR)403

Descriptive statistics

Standard deviation4588.2697
Coefficient of variation (CV)4.6082483
Kurtosis115.53431
Mean995.6646
Median Absolute Deviation (MAD)121
Skewness10.197667
Sum160302
Variance21052219
MonotonicityNot monotonic
2023-12-12T20:33:12.698891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 4
 
2.5%
0 3
 
1.9%
201 3
 
1.9%
48 3
 
1.9%
6 3
 
1.9%
1 2
 
1.2%
33 2
 
1.2%
59 2
 
1.2%
26 2
 
1.2%
11 2
 
1.2%
Other values (122) 135
83.3%
ValueCountFrequency (%)
0 3
1.9%
1 2
1.2%
2 1
 
0.6%
3 2
1.2%
4 1
 
0.6%
6 3
1.9%
9 1
 
0.6%
10 2
1.2%
11 2
1.2%
13 1
 
0.6%
ValueCountFrequency (%)
54240 1
0.6%
16325 1
0.6%
8580 1
0.6%
8160 1
0.6%
6211 1
0.6%
5905 1
0.6%
4786 1
0.6%
4191 1
0.6%
3850 1
0.6%
2630 1
0.6%

Interactions

2023-12-12T20:33:04.105892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:52.710251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:54.068183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:55.320830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:56.581151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:57.965935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:59.503718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:00.929401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:02.821218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:04.342093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:52.855504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:54.219806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:55.462553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:56.731617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:58.148396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:59.640273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:01.086842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:02.970575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:04.559796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:53.010537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:54.359623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:55.602335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:56.880383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:58.362127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:59.799749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:01.234953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:03.110957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:04.748015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:53.172340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:54.502437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:55.747001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:57.049650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:58.578800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:59.974804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:01.419137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:03.266131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:04.908282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:53.327716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:54.655040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:55.883500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:57.191491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:58.728326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:00.160389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:01.581291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:03.390507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:05.084566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:53.483110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:54.822224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:56.019588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:57.351649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:58.881243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:00.331404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:01.744555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:03.515474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:05.222156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:53.623923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:54.941106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:56.144176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:57.505998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:59.014715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:00.471921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:01.870563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:03.633263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:05.371942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:53.756696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:55.071796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:56.290317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:57.652448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:59.176332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:00.641532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:02.032841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:03.778925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:05.520591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:53.918541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:55.197377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:56.420321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:57.798527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:59.329606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:00.793698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:02.683260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:03.951591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:33:12.887803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1일 이내2일 이내3일 이내10일 이내1개월 이내3개월 이내6개월 이내1년 이내1년 초과
1일 이내1.0000.9790.9550.9750.8490.7290.3300.0000.000
2일 이내0.9791.0000.9970.9790.8360.6910.4680.2860.000
3일 이내0.9550.9971.0000.9830.9360.8700.6610.5310.000
10일 이내0.9750.9790.9831.0000.8920.7980.5170.4120.287
1개월 이내0.8490.8360.9360.8921.0000.9060.7700.6570.565
3개월 이내0.7290.6910.8700.7980.9061.0000.9680.7340.668
6개월 이내0.3300.4680.6610.5170.7700.9681.0000.9450.750
1년 이내0.0000.2860.5310.4120.6570.7340.9451.0000.949
1년 초과0.0000.0000.0000.2870.5650.6680.7500.9491.000
2023-12-12T20:33:13.123493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1일 이내2일 이내3일 이내10일 이내1개월 이내3개월 이내6개월 이내1년 이내1년 초과
1일 이내1.0000.8720.8420.8260.7780.7160.6120.5240.455
2일 이내0.8721.0000.9290.9190.8680.7770.6340.5130.402
3일 이내0.8420.9291.0000.9520.9110.8170.6780.5490.426
10일 이내0.8260.9190.9521.0000.9610.8770.7320.5960.466
1개월 이내0.7780.8680.9110.9611.0000.9490.8150.6810.540
3개월 이내0.7160.7770.8170.8770.9491.0000.9240.8070.676
6개월 이내0.6120.6340.6780.7320.8150.9241.0000.9320.801
1년 이내0.5240.5130.5490.5960.6810.8070.9321.0000.894
1년 초과0.4550.4020.4260.4660.5400.6760.8010.8941.000

Missing values

2023-12-12T20:33:05.761216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:33:06.045321image/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.
2023-12-12T20:33:06.316297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

범죄분류1일 이내2일 이내3일 이내10일 이내1개월 이내3개월 이내6개월 이내1년 이내1년 초과
0절도2495037843521199272966819153578132884191
1장물1262528152265424263188439
2사기1195244944740732330845997263652342954240
3횡령1704315319208039594221277731178580
4배임1351020913154716542630
5손괴122325558144729679347981153572653
6살인620252453746191637
7강도422443112313398363175
8방화765301010711913538139
9성폭력12540451456269957585220165011821629
범죄분류1일 이내2일 이내3일 이내10일 이내1개월 이내3개월 이내6개월 이내1년 이내1년 초과
152폐기물관리법1373898348429183132201
153풍속영업의규제에관한법률6066455817534
154하천법432123236592240
155학교보건법10101222203210
156학원의설립운영및과외교습에관한법률110471061206465106
157화물자동차운수사업법1294041460720551682948
158화재로인한재해보상과보험가입에관한법률500237136
159화재예방,소방시설설치유지및안전관리에관한법률2807101248935725151
160화학물질관리법113339641029555879106902433243336211
161기타특별법<NA><NA><NA><NA><NA><NA><NA><NA><NA>