Overview

Dataset statistics

Number of variables11
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory98.4 B

Variable types

Numeric5
Categorical2
Text4

Dataset

Description전라남도 내에서 발생한 5대범죄(살인,강도,강간,절도,폭력) 발생 및 검거 현황을 연도별로 정리한 데이터를 제공합니다.
Author경찰청 전라남도경찰청
URLhttps://www.data.go.kr/data/15078210/fileData.do

Alerts

성년(남) is highly overall correlated with 성년(여) and 4 other fieldsHigh correlation
성년(여) is highly overall correlated with 성년(남) and 4 other fieldsHigh correlation
미성년(남) is highly overall correlated with 성년(남) and 3 other fieldsHigh correlation
미성년(여) is highly overall correlated with 성년(남) and 4 other fieldsHigh correlation
분류 is highly overall correlated with 성년(남) and 2 other fieldsHigh correlation
법인체(기타) is highly overall correlated with 성년(남) and 3 other fieldsHigh correlation
미성년(남) has 6 (20.0%) zerosZeros
미성년(여) has 8 (26.7%) zerosZeros

Reproduction

Analysis started2024-05-11 10:18:49.381166
Analysis finished2024-05-11 10:18:59.826244
Duration10.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.5
Minimum2018
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-05-11T10:19:00.061537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2018
5-th percentile2018
Q12019
median2020.5
Q32022
95-th percentile2023
Maximum2023
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7370208
Coefficient of variation (CV)0.00085969851
Kurtosis-1.2783673
Mean2020.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum60615
Variance3.0172414
MonotonicityDecreasing
2024-05-11T10:19:00.483480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2023 5
16.7%
2022 5
16.7%
2021 5
16.7%
2020 5
16.7%
2019 5
16.7%
2018 5
16.7%
ValueCountFrequency (%)
2018 5
16.7%
2019 5
16.7%
2020 5
16.7%
2021 5
16.7%
2022 5
16.7%
2023 5
16.7%
ValueCountFrequency (%)
2023 5
16.7%
2022 5
16.7%
2021 5
16.7%
2020 5
16.7%
2019 5
16.7%
2018 5
16.7%

분류
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
살인
6 
강도
6 
강간,강제추행
6 
절도
6 
폭력
6 

Length

Max length7
Median length2
Mean length3
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row살인
2nd row강도
3rd row강간,강제추행
4th row절도
5th row폭력

Common Values

ValueCountFrequency (%)
살인 6
20.0%
강도 6
20.0%
강간,강제추행 6
20.0%
절도 6
20.0%
폭력 6
20.0%

Length

2024-05-11T10:19:00.892462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T10:19:01.309827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
살인 6
20.0%
강도 6
20.0%
강간,강제추행 6
20.0%
절도 6
20.0%
폭력 6
20.0%

발생건수
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-05-11T10:19:01.843751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.1333333
Min length2

Characters and Unicode

Total characters94
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)93.3%

Sample

1st row37
2nd row11
3rd row666
4th row5,680
5th row8,870
ValueCountFrequency (%)
27 2
 
6.7%
37 1
 
3.3%
32 1
 
3.3%
4988 1
 
3.3%
749 1
 
3.3%
33 1
 
3.3%
10189 1
 
3.3%
5657 1
 
3.3%
739 1
 
3.3%
25 1
 
3.3%
Other values (19) 19
63.3%
2024-05-11T10:19:02.717609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 12
12.8%
8 12
12.8%
1 10
10.6%
6 10
10.6%
3 10
10.6%
2 9
9.6%
5 9
9.6%
4 8
8.5%
9 7
7.4%
0 5
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 92
97.9%
Other Punctuation 2
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 12
13.0%
8 12
13.0%
1 10
10.9%
6 10
10.9%
3 10
10.9%
2 9
9.8%
5 9
9.8%
4 8
8.7%
9 7
7.6%
0 5
5.4%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 94
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 12
12.8%
8 12
12.8%
1 10
10.6%
6 10
10.6%
3 10
10.6%
2 9
9.6%
5 9
9.6%
4 8
8.5%
9 7
7.4%
0 5
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 12
12.8%
8 12
12.8%
1 10
10.6%
6 10
10.6%
3 10
10.6%
2 9
9.6%
5 9
9.6%
4 8
8.5%
9 7
7.4%
0 5
5.3%

검거건수
Text

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-05-11T10:19:03.234566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.0666667
Min length2

Characters and Unicode

Total characters92
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)83.3%

Sample

1st row37
2nd row10
3rd row650
4th row4,027
5th row8,141
ValueCountFrequency (%)
28 3
 
10.0%
33 2
 
6.7%
15 1
 
3.3%
37 1
 
3.3%
32 1
 
3.3%
3026 1
 
3.3%
715 1
 
3.3%
26 1
 
3.3%
9246 1
 
3.3%
3638 1
 
3.3%
Other values (17) 17
56.7%
2024-05-11T10:19:04.098403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 13
14.1%
1 12
13.0%
6 11
12.0%
7 11
12.0%
2 10
10.9%
4 9
9.8%
8 8
8.7%
5 6
6.5%
0 5
 
5.4%
9 5
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 90
97.8%
Other Punctuation 2
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 13
14.4%
1 12
13.3%
6 11
12.2%
7 11
12.2%
2 10
11.1%
4 9
10.0%
8 8
8.9%
5 6
6.7%
0 5
 
5.6%
9 5
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 92
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 13
14.1%
1 12
13.0%
6 11
12.0%
7 11
12.0%
2 10
10.9%
4 9
9.8%
8 8
8.7%
5 6
6.5%
0 5
 
5.4%
9 5
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 13
14.1%
1 12
13.0%
6 11
12.0%
7 11
12.0%
2 10
10.9%
4 9
9.8%
8 8
8.7%
5 6
6.5%
0 5
 
5.4%
9 5
 
5.4%

검거인원(남)
Text

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-05-11T10:19:04.566485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.1666667
Min length2

Characters and Unicode

Total characters95
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)86.7%

Sample

1st row35
2nd row16
3rd row655
4th row2,717
5th row8,630
ValueCountFrequency (%)
28 2
 
6.7%
29 2
 
6.7%
35 1
 
3.3%
17 1
 
3.3%
2513 1
 
3.3%
796 1
 
3.3%
31 1
 
3.3%
10495 1
 
3.3%
2863 1
 
3.3%
780 1
 
3.3%
Other values (18) 18
60.0%
2024-05-11T10:19:05.600984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 16
16.8%
1 12
12.6%
6 10
10.5%
7 10
10.5%
5 9
9.5%
3 9
9.5%
8 8
8.4%
9 7
7.4%
0 6
 
6.3%
4 6
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 93
97.9%
Other Punctuation 2
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 16
17.2%
1 12
12.9%
6 10
10.8%
7 10
10.8%
5 9
9.7%
3 9
9.7%
8 8
8.6%
9 7
7.5%
0 6
 
6.5%
4 6
 
6.5%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 95
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 16
16.8%
1 12
12.6%
6 10
10.5%
7 10
10.5%
5 9
9.5%
3 9
9.5%
8 8
8.4%
9 7
7.4%
0 6
 
6.3%
4 6
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 16
16.8%
1 12
12.6%
6 10
10.5%
7 10
10.5%
5 9
9.5%
3 9
9.5%
8 8
8.4%
9 7
7.4%
0 6
 
6.3%
4 6
 
6.3%

검거인원(여)
Text

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-05-11T10:19:05.956639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.2333333
Min length1

Characters and Unicode

Total characters67
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)76.7%

Sample

1st row2
2nd row2
3rd row27
4th row878
5th row1,926
ValueCountFrequency (%)
5 3
 
10.0%
2 2
 
6.7%
3 2
 
6.7%
7 1
 
3.3%
4 1
 
3.3%
592 1
 
3.3%
16 1
 
3.3%
8 1
 
3.3%
2176 1
 
3.3%
671 1
 
3.3%
Other values (16) 16
53.3%
2024-05-11T10:19:07.062152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 14
20.9%
1 10
14.9%
5 7
10.4%
7 7
10.4%
8 7
10.4%
9 5
 
7.5%
6 5
 
7.5%
4 4
 
6.0%
0 4
 
6.0%
3 3
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66
98.5%
Other Punctuation 1
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 14
21.2%
1 10
15.2%
5 7
10.6%
7 7
10.6%
8 7
10.6%
9 5
 
7.6%
6 5
 
7.6%
4 4
 
6.1%
0 4
 
6.1%
3 3
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 67
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 14
20.9%
1 10
14.9%
5 7
10.4%
7 7
10.4%
8 7
10.4%
9 5
 
7.5%
6 5
 
7.5%
4 4
 
6.0%
0 4
 
6.0%
3 3
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 14
20.9%
1 10
14.9%
5 7
10.4%
7 7
10.4%
8 7
10.4%
9 5
 
7.5%
6 5
 
7.5%
4 4
 
6.0%
0 4
 
6.0%
3 3
 
4.5%

성년(남)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2325
Minimum9
Maximum10063
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-05-11T10:19:07.609262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile12.45
Q128
median671
Q32190.25
95-th percentile9456.8
Maximum10063
Range10054
Interquartile range (IQR)2162.25

Descriptive statistics

Standard deviation3413.7794
Coefficient of variation (CV)1.4682922
Kurtosis0.50497341
Mean2325
Median Absolute Deviation (MAD)656
Skewness1.4493729
Sum69750
Variance11653890
MonotonicityNot monotonic
2024-05-11T10:19:08.222940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
26 3
 
10.0%
28 2
 
6.7%
35 1
 
3.3%
13 1
 
3.3%
9986 1
 
3.3%
2074 1
 
3.3%
700 1
 
3.3%
29 1
 
3.3%
10063 1
 
3.3%
2313 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
9 1
 
3.3%
12 1
 
3.3%
13 1
 
3.3%
17 1
 
3.3%
26 3
10.0%
28 2
6.7%
29 1
 
3.3%
32 1
 
3.3%
35 1
 
3.3%
578 1
 
3.3%
ValueCountFrequency (%)
10063 1
3.3%
9986 1
3.3%
8810 1
3.3%
8094 1
3.3%
7970 1
3.3%
7944 1
3.3%
2313 1
3.3%
2229 1
3.3%
2074 1
3.3%
2037 1
3.3%

성년(여)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean502.3
Minimum1
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-05-11T10:19:08.821976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.45
Q15
median20.5
Q3740.75
95-th percentile1884.9
Maximum2018
Range2017
Interquartile range (IQR)735.75

Descriptive statistics

Standard deviation720.39501
Coefficient of variation (CV)1.4341927
Kurtosis-0.19478106
Mean502.3
Median Absolute Deviation (MAD)18.5
Skewness1.1741188
Sum15069
Variance518968.98
MonotonicityNot monotonic
2024-05-11T10:19:09.993592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5 4
 
13.3%
2 2
 
6.7%
24 2
 
6.7%
3 2
 
6.7%
1 2
 
6.7%
656 1
 
3.3%
1893 1
 
3.3%
543 1
 
3.3%
13 1
 
3.3%
6 1
 
3.3%
Other values (13) 13
43.3%
ValueCountFrequency (%)
1 2
6.7%
2 2
6.7%
3 2
6.7%
5 4
13.3%
6 1
 
3.3%
7 1
 
3.3%
13 1
 
3.3%
17 1
 
3.3%
20 1
 
3.3%
21 1
 
3.3%
ValueCountFrequency (%)
2018 1
3.3%
1893 1
3.3%
1875 1
3.3%
1734 1
3.3%
1703 1
3.3%
1680 1
3.3%
794 1
3.3%
769 1
3.3%
656 1
3.3%
625 1
3.3%

미성년(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.63333
Minimum0
Maximum814
Zeros6
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-05-11T10:19:10.659106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.25
median77
Q3463.75
95-th percentile696.45
Maximum814
Range814
Interquartile range (IQR)460.5

Descriptive statistics

Standard deviation275.71793
Coefficient of variation (CV)1.1651695
Kurtosis-1.0743093
Mean236.63333
Median Absolute Deviation (MAD)77
Skewness0.71249002
Sum7099
Variance76020.378
MonotonicityNot monotonic
2024-05-11T10:19:11.049198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 6
20.0%
77 2
 
6.7%
5 2
 
6.7%
3 2
 
6.7%
52 1
 
3.3%
502 1
 
3.3%
439 1
 
3.3%
96 1
 
3.3%
432 1
 
3.3%
550 1
 
3.3%
Other values (12) 12
40.0%
ValueCountFrequency (%)
0 6
20.0%
3 2
 
6.7%
4 1
 
3.3%
5 2
 
6.7%
7 1
 
3.3%
52 1
 
3.3%
72 1
 
3.3%
77 2
 
6.7%
87 1
 
3.3%
96 1
 
3.3%
ValueCountFrequency (%)
814 1
3.3%
705 1
3.3%
686 1
3.3%
684 1
3.3%
550 1
3.3%
506 1
3.3%
502 1
3.3%
472 1
3.3%
439 1
3.3%
432 1
3.3%

미성년(여)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.266667
Minimum0
Maximum192
Zeros8
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-05-11T10:19:11.463673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median3.5
Q383.25
95-th percentile152.6
Maximum192
Range192
Interquartile range (IQR)83

Descriptive statistics

Standard deviation59.465396
Coefficient of variation (CV)1.3433448
Kurtosis-0.11542378
Mean44.266667
Median Absolute Deviation (MAD)3.5
Skewness1.0783015
Sum1328
Variance3536.1333
MonotonicityNot monotonic
2024-05-11T10:19:12.127647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 8
26.7%
1 3
 
10.0%
84 2
 
6.7%
3 2
 
6.7%
2 2
 
6.7%
132 1
 
3.3%
131 1
 
3.3%
49 1
 
3.3%
158 1
 
3.3%
56 1
 
3.3%
Other values (8) 8
26.7%
ValueCountFrequency (%)
0 8
26.7%
1 3
 
10.0%
2 2
 
6.7%
3 2
 
6.7%
4 1
 
3.3%
5 1
 
3.3%
10 1
 
3.3%
49 1
 
3.3%
56 1
 
3.3%
76 1
 
3.3%
ValueCountFrequency (%)
192 1
3.3%
158 1
3.3%
146 1
3.3%
132 1
3.3%
131 1
3.3%
107 1
3.3%
84 2
6.7%
81 1
3.3%
76 1
3.3%
56 1
3.3%

법인체(기타)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
23 
1
4 
2
3 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 23
76.7%
1 4
 
13.3%
2 3
 
10.0%

Length

2024-05-11T10:19:12.684506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T10:19:13.010711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 23
76.7%
1 4
 
13.3%
2 3
 
10.0%

Interactions

2024-05-11T10:18:57.012426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:50.447825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:52.156015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:53.603178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:55.470280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:57.291060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:50.760111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:52.455670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:53.865117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:55.728269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:57.556490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:51.050118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:52.749354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:54.111870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:55.982286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:58.016300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:51.390408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:53.029399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:54.465392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:56.343293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:58.385090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:51.729905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:53.299639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:54.827423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:18:56.675816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T10:19:13.267934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도분류발생건수검거건수검거인원(남)검거인원(여)성년(남)성년(여)미성년(남)미성년(여)법인체(기타)
연도1.0000.0001.0000.8750.8490.9480.0000.0000.0000.0000.000
분류0.0001.0001.0000.9280.9290.9050.7570.9380.6440.7540.485
발생건수1.0001.0001.0000.9850.9840.9801.0001.0001.0001.0001.000
검거건수0.8750.9280.9851.0000.9830.9951.0001.0001.0001.0001.000
검거인원(남)0.8490.9290.9840.9831.0000.9811.0001.0001.0001.0001.000
검거인원(여)0.9480.9050.9800.9950.9811.0001.0001.0001.0001.0001.000
성년(남)0.0000.7571.0001.0001.0001.0001.0000.8640.7160.9930.949
성년(여)0.0000.9381.0001.0001.0001.0000.8641.0000.7420.9160.629
미성년(남)0.0000.6441.0001.0001.0001.0000.7160.7421.0000.8430.658
미성년(여)0.0000.7541.0001.0001.0001.0000.9930.9160.8431.0000.958
법인체(기타)0.0000.4851.0001.0001.0001.0000.9490.6290.6580.9581.000
2024-05-11T10:19:13.872376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류법인체(기타)
분류1.0000.398
법인체(기타)0.3981.000
2024-05-11T10:19:14.123342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도성년(남)성년(여)미성년(남)미성년(여)분류법인체(기타)
연도1.000-0.107-0.0580.0590.1100.0000.000
성년(남)-0.1071.0000.9470.7850.8310.6160.683
성년(여)-0.0580.9471.0000.8270.8940.6480.564
미성년(남)0.0590.7850.8271.0000.8870.4590.514
미성년(여)0.1100.8310.8940.8871.0000.5100.664
분류0.0000.6160.6480.4590.5101.0000.398
법인체(기타)0.0000.6830.5640.5140.6640.3981.000

Missing values

2024-05-11T10:18:59.115041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T10:18:59.601182image/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

연도분류발생건수검거건수검거인원(남)검거인원(여)성년(남)성년(여)미성년(남)미성년(여)법인체(기타)
02023살인3737352352000
12023강도111016291710
22023강간,강제추행666650655275781777100
32023절도5,6804,0272,7178782033794684840
42023폭력8,8708,1418,6301,926794417346861921
52022살인2928285285000
62022강도1314229175540
72022강간,강제추행67564369824626217230
82022절도6152387727428502037769705810
92022폭력8727791085661826809416804721461
연도분류발생건수검거건수검거인원(남)검거인원(여)성년(남)성년(여)미성년(남)미성년(여)법인체(기타)
202019살인2528263263000
212019강도2728293263300
222019강간,강제추행73971978025693248710
232019절도5657363828636712313615550560
242019폭력1018992461049521761006320184321580
252018살인3333295295000
262018강도2726318266520
272018강간,강제추행74971579616700139630
282018절도4988302625135922074543439492
292018폭력100359166104482024998618935021312