Overview

Dataset statistics

Number of variables9
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory80.7 B

Variable types

Categorical3
Numeric6

Dataset

Description2019년~2021년 대전광역시경찰서 각 경찰서별 5대범죄(살인,강도,강간 및 강제추행,절도,폭행)발생 및 검거 현황
Author경찰청 대전광역시경찰청
URLhttps://www.data.go.kr/data/15073507/fileData.do

Alerts

계 is highly overall correlated with 강간강제추행 and 3 other fieldsHigh correlation
강간강제추행 is highly overall correlated with 계 and 1 other fieldsHigh correlation
절도 is highly overall correlated with 계 and 2 other fieldsHigh correlation
폭력 is highly overall correlated with 계 and 2 other fieldsHigh correlation
구분 is highly overall correlated with 계 and 1 other fieldsHigh correlation
계 has unique valuesUnique
폭력 has unique valuesUnique
살인 has 7 (14.6%) zerosZeros
강도 has 3 (6.2%) zerosZeros

Reproduction

Analysis started2024-03-14 12:36:59.879562
Analysis finished2024-03-14 12:37:10.600277
Duration10.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도별
Categorical

Distinct4
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size512.0 B
2019년
12 
2020년
12 
2021년
12 
2022년
12 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019년
2nd row2019년
3rd row2019년
4th row2019년
5th row2019년

Common Values

ValueCountFrequency (%)
2019년 12
25.0%
2020년 12
25.0%
2021년 12
25.0%
2022년 12
25.0%

Length

2024-03-14T21:37:10.804942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:37:11.117585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019년 12
25.0%
2020년 12
25.0%
2021년 12
25.0%
2022년 12
25.0%

관서명
Categorical

Distinct6
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size512.0 B
대전중부경찰서
8 
대전동부경찰서
8 
대전서부경찰서
8 
대전대덕경찰서
8 
대전둔산경찰서
8 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대전중부경찰서
2nd row대전중부경찰서
3rd row대전동부경찰서
4th row대전동부경찰서
5th row대전서부경찰서

Common Values

ValueCountFrequency (%)
대전중부경찰서 8
16.7%
대전동부경찰서 8
16.7%
대전서부경찰서 8
16.7%
대전대덕경찰서 8
16.7%
대전둔산경찰서 8
16.7%
대전유성경찰서 8
16.7%

Length

2024-03-14T21:37:11.467155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:37:11.793609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전중부경찰서 8
16.7%
대전동부경찰서 8
16.7%
대전서부경찰서 8
16.7%
대전대덕경찰서 8
16.7%
대전둔산경찰서 8
16.7%
대전유성경찰서 8
16.7%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size512.0 B
발생건수
24 
검거건수
24 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row발생건수
2nd row검거건수
3rd row발생건수
4th row검거건수
5th row발생건수

Common Values

ValueCountFrequency (%)
발생건수 24
50.0%
검거건수 24
50.0%

Length

2024-03-14T21:37:12.177479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:37:12.471153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
발생건수 24
50.0%
검거건수 24
50.0%

계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2111.6042
Minimum1282
Maximum3039
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-03-14T21:37:12.802092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1282
5-th percentile1492.3
Q11825.75
median2053.5
Q32396.75
95-th percentile2724.4
Maximum3039
Range1757
Interquartile range (IQR)571

Descriptive statistics

Standard deviation406.86083
Coefficient of variation (CV)0.19267855
Kurtosis-0.47718081
Mean2111.6042
Median Absolute Deviation (MAD)263.5
Skewness0.10617639
Sum101357
Variance165535.73
MonotonicityNot monotonic
2024-03-14T21:37:13.238515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
2927 1
 
2.1%
1919 1
 
2.1%
1607 1
 
2.1%
2076 1
 
2.1%
1606 1
 
2.1%
1663 1
 
2.1%
1388 1
 
2.1%
2210 1
 
2.1%
1709 1
 
2.1%
2581 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
1282 1
2.1%
1388 1
2.1%
1486 1
2.1%
1504 1
2.1%
1581 1
2.1%
1606 1
2.1%
1607 1
2.1%
1663 1
2.1%
1709 1
2.1%
1815 1
2.1%
ValueCountFrequency (%)
3039 1
2.1%
2927 1
2.1%
2765 1
2.1%
2649 1
2.1%
2608 1
2.1%
2602 1
2.1%
2581 1
2.1%
2516 1
2.1%
2514 1
2.1%
2502 1
2.1%

살인
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9166667
Minimum0
Maximum8
Zeros7
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-03-14T21:37:13.608028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34.25
95-th percentile6
Maximum8
Range8
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation2.1618878
Coefficient of variation (CV)0.74121867
Kurtosis-0.43927623
Mean2.9166667
Median Absolute Deviation (MAD)2
Skewness0.50745042
Sum140
Variance4.6737589
MonotonicityNot monotonic
2024-03-14T21:37:13.971776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 9
18.8%
3 8
16.7%
1 7
14.6%
0 7
14.6%
5 5
10.4%
6 5
10.4%
4 5
10.4%
8 2
 
4.2%
ValueCountFrequency (%)
0 7
14.6%
1 7
14.6%
2 9
18.8%
3 8
16.7%
4 5
10.4%
5 5
10.4%
6 5
10.4%
8 2
 
4.2%
ValueCountFrequency (%)
8 2
 
4.2%
6 5
10.4%
5 5
10.4%
4 5
10.4%
3 8
16.7%
2 9
18.8%
1 7
14.6%
0 7
14.6%

강도
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6666667
Minimum0
Maximum8
Zeros3
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-03-14T21:37:14.323429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.35
Q11
median2
Q34
95-th percentile7
Maximum8
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0559549
Coefficient of variation (CV)0.77098307
Kurtosis0.69113397
Mean2.6666667
Median Absolute Deviation (MAD)1
Skewness1.145654
Sum128
Variance4.2269504
MonotonicityNot monotonic
2024-03-14T21:37:14.700517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 14
29.2%
1 13
27.1%
4 7
14.6%
3 4
 
8.3%
0 3
 
6.2%
6 2
 
4.2%
8 2
 
4.2%
7 2
 
4.2%
5 1
 
2.1%
ValueCountFrequency (%)
0 3
 
6.2%
1 13
27.1%
2 14
29.2%
3 4
 
8.3%
4 7
14.6%
5 1
 
2.1%
6 2
 
4.2%
7 2
 
4.2%
8 2
 
4.2%
ValueCountFrequency (%)
8 2
 
4.2%
7 2
 
4.2%
6 2
 
4.2%
5 1
 
2.1%
4 7
14.6%
3 4
 
8.3%
2 14
29.2%
1 13
27.1%
0 3
 
6.2%

강간강제추행
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)85.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.35417
Minimum44
Maximum188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-03-14T21:37:15.080700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile58.05
Q184.75
median108
Q3127.75
95-th percentile156.6
Maximum188
Range144
Interquartile range (IQR)43

Descriptive statistics

Standard deviation32.404025
Coefficient of variation (CV)0.30184227
Kurtosis-0.39110229
Mean107.35417
Median Absolute Deviation (MAD)22
Skewness0.12837229
Sum5153
Variance1050.0208
MonotonicityNot monotonic
2024-03-14T21:37:15.495550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
127 3
 
6.2%
74 2
 
4.2%
100 2
 
4.2%
130 2
 
4.2%
122 2
 
4.2%
110 2
 
4.2%
141 1
 
2.1%
84 1
 
2.1%
69 1
 
2.1%
87 1
 
2.1%
Other values (31) 31
64.6%
ValueCountFrequency (%)
44 1
2.1%
48 1
2.1%
57 1
2.1%
60 1
2.1%
63 1
2.1%
69 1
2.1%
70 1
2.1%
73 1
2.1%
74 2
4.2%
83 1
2.1%
ValueCountFrequency (%)
188 1
2.1%
162 1
2.1%
158 1
2.1%
154 1
2.1%
150 1
2.1%
147 1
2.1%
146 1
2.1%
141 1
2.1%
140 1
2.1%
137 1
2.1%

절도
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean763.10417
Minimum373
Maximum1313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-03-14T21:37:15.907452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum373
5-th percentile445.15
Q1570.75
median736.5
Q3910.25
95-th percentile1205.25
Maximum1313
Range940
Interquartile range (IQR)339.5

Descriptive statistics

Standard deviation238.41554
Coefficient of variation (CV)0.31242856
Kurtosis-0.55089821
Mean763.10417
Median Absolute Deviation (MAD)169.5
Skewness0.49430131
Sum36629
Variance56841.968
MonotonicityNot monotonic
2024-03-14T21:37:16.328186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1024 2
 
4.2%
1221 1
 
2.1%
1046 1
 
2.1%
615 1
 
2.1%
772 1
 
2.1%
464 1
 
2.1%
572 1
 
2.1%
430 1
 
2.1%
820 1
 
2.1%
500 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
373 1
2.1%
430 1
2.1%
435 1
2.1%
464 1
2.1%
468 1
2.1%
500 1
2.1%
508 1
2.1%
512 1
2.1%
514 1
2.1%
538 1
2.1%
ValueCountFrequency (%)
1313 1
2.1%
1252 1
2.1%
1221 1
2.1%
1176 1
2.1%
1102 1
2.1%
1046 1
2.1%
1035 1
2.1%
1028 1
2.1%
1024 2
4.2%
974 1
2.1%

폭력
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1235.5625
Minimum835
Maximum1605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-03-14T21:37:16.945376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum835
5-th percentile924.4
Q11117.25
median1250
Q31354.25
95-th percentile1535.1
Maximum1605
Range770
Interquartile range (IQR)237

Descriptive statistics

Standard deviation187.26439
Coefficient of variation (CV)0.15156206
Kurtosis-0.54051261
Mean1235.5625
Median Absolute Deviation (MAD)122
Skewness-0.035324664
Sum59307
Variance35067.953
MonotonicityNot monotonic
2024-03-14T21:37:17.402485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1554 1
 
2.1%
1133 1
 
2.1%
916 1
 
2.1%
1200 1
 
2.1%
1051 1
 
2.1%
1015 1
 
2.1%
899 1
 
2.1%
1257 1
 
2.1%
1096 1
 
2.1%
1354 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
835 1
2.1%
899 1
2.1%
916 1
2.1%
940 1
2.1%
987 1
2.1%
1006 1
2.1%
1015 1
2.1%
1050 1
2.1%
1051 1
2.1%
1054 1
2.1%
ValueCountFrequency (%)
1605 1
2.1%
1591 1
2.1%
1554 1
2.1%
1500 1
2.1%
1480 1
2.1%
1476 1
2.1%
1469 1
2.1%
1448 1
2.1%
1433 1
2.1%
1399 1
2.1%

Interactions

2024-03-14T21:37:08.331052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:00.375021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:01.933876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:03.502049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:05.279669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:06.839804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:08.594694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:00.632868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:02.197100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:03.767817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:05.539726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:07.092641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:08.860122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:00.895526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:02.465962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:04.035198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:05.802112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:07.345690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:09.128107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:01.161004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:02.731907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:04.299333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:06.080053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:07.598974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:09.390972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:01.425510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:02.994075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:04.560126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:06.335229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:07.850070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:09.633927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:01.667666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:03.232411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:04.809797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:06.574204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:37:08.077517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T21:37:17.703998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도별관서명구분계살인강도강간강제추행절도폭력
연도별1.0000.0000.0000.0000.6040.5570.0000.0000.000
관서명0.0001.0000.0000.3130.6070.6630.7530.1590.247
구분0.0000.0001.0000.7030.0000.0000.1970.8340.078
계0.0000.3130.7031.0000.0000.0000.5980.8330.910
살인0.6040.6070.0000.0001.0000.5910.3310.0000.000
강도0.5570.6630.0000.0000.5911.0000.0000.2870.149
강간강제추행0.0000.7530.1970.5980.3310.0001.0000.6080.627
절도0.0000.1590.8340.8330.0000.2870.6081.0000.713
폭력0.0000.2470.0780.9100.0000.1490.6270.7131.000
2024-03-14T21:37:17.999588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분연도별관서명
구분1.0000.0000.000
연도별0.0001.0000.000
관서명0.0000.0001.000
2024-03-14T21:37:18.257927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계살인강도강간강제추행절도폭력연도별관서명구분
계1.0000.3270.4600.6350.9220.8500.0000.1990.511
살인0.3271.0000.0320.3590.2960.2660.2860.3800.000
강도0.4600.0321.0000.2340.4020.4310.3650.3840.000
강간강제추행0.6350.3590.2341.0000.4490.6770.0000.4960.122
절도0.9220.2960.4020.4491.0000.6200.0000.1330.614
폭력0.8500.2660.4310.6770.6201.0000.0000.1700.143
연도별0.0000.2860.3650.0000.0000.0001.0000.0000.000
관서명0.1990.3800.3840.4960.1330.1700.0001.0000.000
구분0.5110.0000.0000.1220.6140.1430.0000.0001.000

Missing values

2024-03-14T21:37:09.995924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T21:37:10.435785image/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

연도별관서명구분계살인강도강간강제추행절도폭력
02019년대전중부경찰서발생건수29275614112211554
12019년대전중부경찰서검거건수2254561186771448
22019년대전동부경찰서발생건수2516388311761246
32019년대전동부경찰서검거건수199938747961118
42019년대전서부경찰서발생건수2393141048151469
52019년대전서부경찰서검거건수190914935121299
62019년대전대덕경찰서발생건수182201606461115
72019년대전대덕경찰서검거건수148601444351006
82019년대전둔산경찰서발생건수2649341889741480
92019년대전둔산경찰서검거건수1964241545141290
연도별관서명구분계살인강도강간강제추행절도폭력
382022년대전동부경찰서발생건수26082712212521225
392022년대전동부경찰서검거건수194127927861054
402022년대전서부경찰서발생건수24081210010241281
412022년대전서부경찰서검거건수182113856021130
422022년대전대덕경찰서발생건수15813186551940
432022년대전대덕경찰서검거건수12823170373835
442022년대전둔산경찰서발생건수2329021588471322
452022년대전둔산경찰서검거건수1815011475081159
462022년대전유성경찰서발생건수30393213013131591
472022년대전유성경찰서검거건수2185321067101364