Overview

Dataset statistics

Number of variables7
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory67.5 B

Variable types

Numeric6
Categorical1

Dataset

Description연도별 울산광역시 지역 내 5대 범죄(살인, 강도, 강간, 절도, 폭력) 발생 및 검거 추이를 공개함으로써 국민들의 알 권리를 충족하고자 합니다.
Author경찰청 울산광역시경찰청
URLhttps://www.data.go.kr/data/15007067/fileData.do

Alerts

연도 is highly overall correlated with 살인 and 2 other fieldsHigh correlation
살인 is highly overall correlated with 연도 and 2 other fieldsHigh correlation
강도 is highly overall correlated with 연도 and 2 other fieldsHigh correlation
절도 is highly overall correlated with 폭력 and 1 other fieldsHigh correlation
폭력 is highly overall correlated with 연도 and 3 other fieldsHigh correlation
유형 is highly overall correlated with 절도High correlation
절도 has unique valuesUnique
폭력 has unique valuesUnique

Reproduction

Analysis started2023-12-11 22:49:10.767426
Analysis finished2023-12-11 22:49:14.500219
Duration3.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.5
Minimum2011
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T07:49:14.549488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011.15
Q12013.75
median2016.5
Q32019.25
95-th percentile2021.85
Maximum2022
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.5262987
Coefficient of variation (CV)0.0017487224
Kurtosis-1.2156934
Mean2016.5
Median Absolute Deviation (MAD)3
Skewness0
Sum48396
Variance12.434783
MonotonicityIncreasing
2023-12-12T07:49:14.656782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2011 2
8.3%
2012 2
8.3%
2013 2
8.3%
2014 2
8.3%
2015 2
8.3%
2016 2
8.3%
2017 2
8.3%
2018 2
8.3%
2019 2
8.3%
2020 2
8.3%
Other values (2) 4
16.7%
ValueCountFrequency (%)
2011 2
8.3%
2012 2
8.3%
2013 2
8.3%
2014 2
8.3%
2015 2
8.3%
2016 2
8.3%
2017 2
8.3%
2018 2
8.3%
2019 2
8.3%
2020 2
8.3%
ValueCountFrequency (%)
2022 2
8.3%
2021 2
8.3%
2020 2
8.3%
2019 2
8.3%
2018 2
8.3%
2017 2
8.3%
2016 2
8.3%
2015 2
8.3%
2014 2
8.3%
2013 2
8.3%

유형
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
발생
12 
검거
12 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row발생
2nd row검거
3rd row발생
4th row검거
5th row발생

Common Values

ValueCountFrequency (%)
발생 12
50.0%
검거 12
50.0%

Length

2023-12-12T07:49:14.780957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:49:14.873332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
발생 12
50.0%
검거 12
50.0%

살인
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.541667
Minimum10
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T07:49:14.951412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10.3
Q114
median19
Q322
95-th percentile27.25
Maximum29
Range19
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.1413005
Coefficient of variation (CV)0.27728362
Kurtosis-0.43528665
Mean18.541667
Median Absolute Deviation (MAD)3
Skewness0.093972995
Sum445
Variance26.432971
MonotonicityNot monotonic
2023-12-12T07:49:15.047416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
22 5
20.8%
14 3
12.5%
23 2
 
8.3%
19 2
 
8.3%
20 2
 
8.3%
17 2
 
8.3%
10 2
 
8.3%
12 2
 
8.3%
28 1
 
4.2%
29 1
 
4.2%
Other values (2) 2
 
8.3%
ValueCountFrequency (%)
10 2
 
8.3%
12 2
 
8.3%
14 3
12.5%
16 1
 
4.2%
17 2
 
8.3%
18 1
 
4.2%
19 2
 
8.3%
20 2
 
8.3%
22 5
20.8%
23 2
 
8.3%
ValueCountFrequency (%)
29 1
 
4.2%
28 1
 
4.2%
23 2
 
8.3%
22 5
20.8%
20 2
 
8.3%
19 2
 
8.3%
18 1
 
4.2%
17 2
 
8.3%
16 1
 
4.2%
14 3
12.5%

강도
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.291667
Minimum9
Maximum104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T07:49:15.149532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile10.15
Q122.25
median28.5
Q337.25
95-th percentile75.2
Maximum104
Range95
Interquartile range (IQR)15

Descriptive statistics

Standard deviation21.622209
Coefficient of variation (CV)0.66959099
Kurtosis5.0631205
Mean32.291667
Median Absolute Deviation (MAD)9
Skewness2.0039535
Sum775
Variance467.51993
MonotonicityNot monotonic
2023-12-12T07:49:15.241682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
38 2
 
8.3%
11 2
 
8.3%
32 2
 
8.3%
14 2
 
8.3%
28 2
 
8.3%
25 2
 
8.3%
104 1
 
4.2%
26 1
 
4.2%
9 1
 
4.2%
10 1
 
4.2%
Other values (8) 8
33.3%
ValueCountFrequency (%)
9 1
4.2%
10 1
4.2%
11 2
8.3%
14 2
8.3%
25 2
8.3%
26 1
4.2%
27 1
4.2%
28 2
8.3%
29 1
4.2%
31 1
4.2%
ValueCountFrequency (%)
104 1
4.2%
80 1
4.2%
48 1
4.2%
45 1
4.2%
38 2
8.3%
37 1
4.2%
33 1
4.2%
32 2
8.3%
31 1
4.2%
29 1
4.2%

강간
Real number (ℝ)

Distinct20
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean420.625
Minimum326
Maximum453
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T07:49:15.375475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum326
5-th percentile350.2
Q1411
median430.5
Q3445
95-th percentile449.7
Maximum453
Range127
Interquartile range (IQR)34

Descriptive statistics

Standard deviation33.305976
Coefficient of variation (CV)0.079182113
Kurtosis2.2114337
Mean420.625
Median Absolute Deviation (MAD)16.5
Skewness-1.6168329
Sum10095
Variance1109.288
MonotonicityNot monotonic
2023-12-12T07:49:15.483639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
445 3
 
12.5%
411 2
 
8.3%
448 2
 
8.3%
381 1
 
4.2%
427 1
 
4.2%
436 1
 
4.2%
406 1
 
4.2%
426 1
 
4.2%
432 1
 
4.2%
446 1
 
4.2%
Other values (10) 10
41.7%
ValueCountFrequency (%)
326 1
4.2%
346 1
4.2%
374 1
4.2%
381 1
4.2%
406 1
4.2%
411 2
8.3%
413 1
4.2%
426 1
4.2%
427 1
4.2%
428 1
4.2%
ValueCountFrequency (%)
453 1
 
4.2%
450 1
 
4.2%
448 2
8.3%
446 1
 
4.2%
445 3
12.5%
437 1
 
4.2%
436 1
 
4.2%
432 1
 
4.2%
431 1
 
4.2%
430 1
 
4.2%

절도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3478.9167
Minimum1835
Maximum6761
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T07:49:15.588254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1835
5-th percentile1874.15
Q12422.5
median3151.5
Q33728
95-th percentile6567.15
Maximum6761
Range4926
Interquartile range (IQR)1305.5

Descriptive statistics

Standard deviation1537.9984
Coefficient of variation (CV)0.44209118
Kurtosis0.020065068
Mean3478.9167
Median Absolute Deviation (MAD)779.5
Skewness1.0775795
Sum83494
Variance2365439
MonotonicityNot monotonic
2023-12-12T07:49:15.692238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
6761 1
 
4.2%
2271 1
 
4.2%
2090 1
 
4.2%
3272 1
 
4.2%
1936 1
 
4.2%
3152 1
 
4.2%
1835 1
 
4.2%
3101 1
 
4.2%
1926 1
 
4.2%
3205 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1835 1
4.2%
1865 1
4.2%
1926 1
4.2%
1936 1
4.2%
2090 1
4.2%
2271 1
4.2%
2473 1
4.2%
2587 1
4.2%
2711 1
4.2%
2818 1
4.2%
ValueCountFrequency (%)
6761 1
4.2%
6648 1
4.2%
6109 1
4.2%
5787 1
4.2%
5456 1
4.2%
4217 1
4.2%
3565 1
4.2%
3342 1
4.2%
3272 1
4.2%
3216 1
4.2%

폭력
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6309.7083
Minimum4467
Maximum8257
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T07:49:15.793125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4467
5-th percentile4542.85
Q15446.75
median6304.5
Q37300.5
95-th percentile7779.5
Maximum8257
Range3790
Interquartile range (IQR)1853.75

Descriptive statistics

Standard deviation1119.3475
Coefficient of variation (CV)0.17740084
Kurtosis-1.1223321
Mean6309.7083
Median Absolute Deviation (MAD)1006.5
Skewness-0.06189275
Sum151433
Variance1252938.9
MonotonicityNot monotonic
2023-12-12T07:49:15.906928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
7285 1
 
4.2%
6528 1
 
4.2%
4486 1
 
4.2%
5034 1
 
4.2%
4467 1
 
4.2%
5046 1
 
4.2%
4865 1
 
4.2%
5505 1
 
4.2%
5272 1
 
4.2%
6023 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
4467 1
4.2%
4486 1
4.2%
4865 1
4.2%
5034 1
4.2%
5046 1
4.2%
5272 1
4.2%
5505 1
4.2%
5562 1
4.2%
5765 1
4.2%
6023 1
4.2%
ValueCountFrequency (%)
8257 1
4.2%
7793 1
4.2%
7703 1
4.2%
7637 1
4.2%
7376 1
4.2%
7347 1
4.2%
7285 1
4.2%
7200 1
4.2%
6831 1
4.2%
6656 1
4.2%

Interactions

2023-12-12T07:49:13.727743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:11.001880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:11.532164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:12.053598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:12.785429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:13.277117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:13.816306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:11.093698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:11.615664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:12.137468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:12.879078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:13.344404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:13.918082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:11.181515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:11.717590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:12.225159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:12.957747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:13.419264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:14.008971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:11.259501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:11.798962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:12.307778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:13.032170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:13.487203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:14.134418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:11.343664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:11.885338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:12.400671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:13.117035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:13.560078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:14.216675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:11.431433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:11.968553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:12.703864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:13.199684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:13.629725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:49:15.997970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도유형살인강도강간절도폭력
연도1.0000.0000.8980.9600.5760.3190.130
유형0.0001.0000.0000.0000.4870.9560.193
살인0.8980.0001.0000.8290.6860.0000.401
강도0.9600.0000.8291.0000.6390.5130.631
강간0.5760.4870.6860.6391.0000.2230.000
절도0.3190.9560.0000.5130.2231.0000.524
폭력0.1300.1930.4010.6310.0000.5241.000
2023-12-12T07:49:16.094679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도살인강도강간절도폭력유형
연도1.000-0.766-0.9460.357-0.467-0.6890.000
살인-0.7661.0000.769-0.1770.4650.6050.000
강도-0.9460.7691.000-0.2110.4060.6190.000
강간0.357-0.177-0.2111.0000.0130.0230.436
절도-0.4670.4650.4060.0131.0000.7420.704
폭력-0.6890.6050.6190.0230.7421.0000.203
유형0.0000.0000.0000.4360.7040.2031.000

Missing values

2023-12-12T07:49:14.347969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:49:14.461847image/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

연도유형살인강도강간절도폭력
02011발생2810438167617285
12011검거298032631515765
22012발생233737461097376
32012검거193834624736186
42013발생184545066487703
52013검거204841128186216
62014발생233244557877637
72014검거223844827116656
82015발생222944554568257
92015검거223143732167200
연도유형살인강도강간절도폭력
142018발생102642733426393
152018검거102743118655562
162019발생192845332056023
172019검거202844819265272
182020발생121444631015505
192020검거121443218354865
202021발생141142631525046
212021검거161140619364467
222022발생141044532725034
232022검거14943620904486