Overview

Dataset statistics

Number of variables11
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory102.0 B

Variable types

Text1
Numeric10

Dataset

Description2022년 경기도남부경찰찰청 5대범죄 관서별, 죄종별 발생, 검거 건수 입니다. 관서별, 죄종별 건수를 숫자데이터 형식으로 제공합니다.
Author경찰청 경기도남부경찰청
URLhttps://www.data.go.kr/data/15126908/fileData.do

Alerts

살인 발생 is highly overall correlated with 살인 검거 and 5 other fieldsHigh correlation
살인 검거 is highly overall correlated with 살인 발생 and 5 other fieldsHigh correlation
강도 발생 is highly overall correlated with 강도 검거High correlation
강도 검거 is highly overall correlated with 강도 발생High correlation
강간 강제추행 발생 is highly overall correlated with 살인 발생 and 6 other fieldsHigh correlation
강간 강제추행 검거 is highly overall correlated with 강간 강제추행 발생 and 4 other fieldsHigh correlation
절도 발생 is highly overall correlated with 살인 발생 and 6 other fieldsHigh correlation
절도 검거 is highly overall correlated with 살인 발생 and 6 other fieldsHigh correlation
폭력 발생 is highly overall correlated with 살인 발생 and 6 other fieldsHigh correlation
폭력 검거 is highly overall correlated with 살인 발생 and 6 other fieldsHigh correlation
관서명 has unique valuesUnique
절도 발생 has unique valuesUnique
절도 검거 has unique valuesUnique
폭력 발생 has unique valuesUnique
폭력 검거 has unique valuesUnique
살인 발생 has 2 (6.2%) zerosZeros
살인 검거 has 1 (3.1%) zerosZeros
강도 발생 has 8 (25.0%) zerosZeros
강도 검거 has 7 (21.9%) zerosZeros
강간 강제추행 발생 has 1 (3.1%) zerosZeros
절도 발생 has 1 (3.1%) zerosZeros
폭력 발생 has 1 (3.1%) zerosZeros

Reproduction

Analysis started2024-03-14 17:59:38.571393
Analysis finished2024-03-14 18:00:06.245830
Duration27.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관서명
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
2024-03-15T03:00:06.876256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.0625
Min length2

Characters and Unicode

Total characters98
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row도경찰청
2nd row수원중부
3rd row수원남부
4th row수원서부
5th row안양동안
ValueCountFrequency (%)
도경찰청 1
 
3.1%
수원중부 1
 
3.1%
여주 1
 
3.1%
안성 1
 
3.1%
이천 1
 
3.1%
의왕 1
 
3.1%
과천 1
 
3.1%
하남 1
 
3.1%
김포 1
 
3.1%
광주 1
 
3.1%
Other values (22) 22
68.8%
2024-03-15T03:00:08.027275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 9
 
9.2%
안 7
 
7.1%
원 6
 
6.1%
성 5
 
5.1%
천 5
 
5.1%
수 4
 
4.1%
남 4
 
4.1%
서 3
 
3.1%
산 3
 
3.1%
양 3
 
3.1%
Other values (37) 49
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 98
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 9
 
9.2%
안 7
 
7.1%
원 6
 
6.1%
성 5
 
5.1%
천 5
 
5.1%
수 4
 
4.1%
남 4
 
4.1%
서 3
 
3.1%
산 3
 
3.1%
양 3
 
3.1%
Other values (37) 49
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 98
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 9
 
9.2%
안 7
 
7.1%
원 6
 
6.1%
성 5
 
5.1%
천 5
 
5.1%
수 4
 
4.1%
남 4
 
4.1%
서 3
 
3.1%
산 3
 
3.1%
양 3
 
3.1%
Other values (37) 49
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 98
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 9
 
9.2%
안 7
 
7.1%
원 6
 
6.1%
성 5
 
5.1%
천 5
 
5.1%
수 4
 
4.1%
남 4
 
4.1%
서 3
 
3.1%
산 3
 
3.1%
양 3
 
3.1%
Other values (37) 49
50.0%

살인 발생
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.875
Minimum0
Maximum9
Zeros2
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T03:00:08.290252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.55
Q12
median3
Q35.25
95-th percentile8.45
Maximum9
Range9
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation2.5621312
Coefficient of variation (CV)0.66119514
Kurtosis-0.52608521
Mean3.875
Median Absolute Deviation (MAD)1
Skewness0.65133124
Sum124
Variance6.5645161
MonotonicityNot monotonic
2024-03-15T03:00:08.648737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 8
25.0%
2 7
21.9%
8 3
 
9.4%
5 3
 
9.4%
0 2
 
6.2%
6 2
 
6.2%
1 2
 
6.2%
4 2
 
6.2%
9 2
 
6.2%
7 1
 
3.1%
ValueCountFrequency (%)
0 2
 
6.2%
1 2
 
6.2%
2 7
21.9%
3 8
25.0%
4 2
 
6.2%
5 3
 
9.4%
6 2
 
6.2%
7 1
 
3.1%
8 3
 
9.4%
9 2
 
6.2%
ValueCountFrequency (%)
9 2
 
6.2%
8 3
 
9.4%
7 1
 
3.1%
6 2
 
6.2%
5 3
 
9.4%
4 2
 
6.2%
3 8
25.0%
2 7
21.9%
1 2
 
6.2%
0 2
 
6.2%

살인 검거
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.71875
Minimum0
Maximum9
Zeros1
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T03:00:08.960370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35.25
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation2.5682224
Coefficient of variation (CV)0.69061442
Kurtosis-0.29263216
Mean3.71875
Median Absolute Deviation (MAD)1.5
Skewness0.85767102
Sum119
Variance6.5957661
MonotonicityNot monotonic
2024-03-15T03:00:09.362327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 8
25.0%
3 8
25.0%
1 4
12.5%
6 3
 
9.4%
9 3
 
9.4%
5 3
 
9.4%
7 1
 
3.1%
8 1
 
3.1%
0 1
 
3.1%
ValueCountFrequency (%)
0 1
 
3.1%
1 4
12.5%
2 8
25.0%
3 8
25.0%
5 3
 
9.4%
6 3
 
9.4%
7 1
 
3.1%
8 1
 
3.1%
9 3
 
9.4%
ValueCountFrequency (%)
9 3
 
9.4%
8 1
 
3.1%
7 1
 
3.1%
6 3
 
9.4%
5 3
 
9.4%
3 8
25.0%
2 8
25.0%
1 4
12.5%
0 1
 
3.1%

강도 발생
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.75
Minimum0
Maximum8
Zeros8
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T03:00:09.837527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median2
Q34.25
95-th percentile7.45
Maximum8
Range8
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.5272706
Coefficient of variation (CV)0.9190075
Kurtosis-0.67953094
Mean2.75
Median Absolute Deviation (MAD)2
Skewness0.64588383
Sum88
Variance6.3870968
MonotonicityNot monotonic
2024-03-15T03:00:10.490353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 8
25.0%
1 5
15.6%
3 4
12.5%
2 4
12.5%
6 3
 
9.4%
4 3
 
9.4%
5 2
 
6.2%
8 2
 
6.2%
7 1
 
3.1%
ValueCountFrequency (%)
0 8
25.0%
1 5
15.6%
2 4
12.5%
3 4
12.5%
4 3
 
9.4%
5 2
 
6.2%
6 3
 
9.4%
7 1
 
3.1%
8 2
 
6.2%
ValueCountFrequency (%)
8 2
 
6.2%
7 1
 
3.1%
6 3
 
9.4%
5 2
 
6.2%
4 3
 
9.4%
3 4
12.5%
2 4
12.5%
1 5
15.6%
0 8
25.0%

강도 검거
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6875
Minimum0
Maximum8
Zeros7
Zeros (%)21.9%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T03:00:11.111721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile7.45
Maximum8
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5455527
Coefficient of variation (CV)0.94718241
Kurtosis-0.64390241
Mean2.6875
Median Absolute Deviation (MAD)2
Skewness0.74950438
Sum86
Variance6.4798387
MonotonicityNot monotonic
2024-03-15T03:00:11.472938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 8
25.0%
0 7
21.9%
4 4
12.5%
6 4
12.5%
3 3
 
9.4%
2 3
 
9.4%
8 2
 
6.2%
7 1
 
3.1%
ValueCountFrequency (%)
0 7
21.9%
1 8
25.0%
2 3
 
9.4%
3 3
 
9.4%
4 4
12.5%
6 4
12.5%
7 1
 
3.1%
8 2
 
6.2%
ValueCountFrequency (%)
8 2
 
6.2%
7 1
 
3.1%
6 4
12.5%
4 4
12.5%
3 3
 
9.4%
2 3
 
9.4%
1 8
25.0%
0 7
21.9%

강간 강제추행 발생
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.15625
Minimum0
Maximum276
Zeros1
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T03:00:12.463347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20.6
Q174.75
median106.5
Q3138.75
95-th percentile255
Maximum276
Range276
Interquartile range (IQR)64

Descriptive statistics

Standard deviation69.05683
Coefficient of variation (CV)0.58445347
Kurtosis0.064962292
Mean118.15625
Median Absolute Deviation (MAD)34
Skewness0.64823121
Sum3781
Variance4768.8458
MonotonicityNot monotonic
2024-03-15T03:00:12.798148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
132 2
 
6.2%
255 2
 
6.2%
207 1
 
3.1%
50 1
 
3.1%
43 1
 
3.1%
91 1
 
3.1%
85 1
 
3.1%
26 1
 
3.1%
14 1
 
3.1%
77 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
0 1
3.1%
14 1
3.1%
26 1
3.1%
43 1
3.1%
50 1
3.1%
61 1
3.1%
67 1
3.1%
68 1
3.1%
77 1
3.1%
85 1
3.1%
ValueCountFrequency (%)
276 1
3.1%
255 2
6.2%
207 1
3.1%
202 1
3.1%
201 1
3.1%
177 1
3.1%
144 1
3.1%
137 1
3.1%
136 1
3.1%
132 2
6.2%

강간 강제추행 검거
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.09375
Minimum5
Maximum382
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T03:00:13.014428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile15.4
Q169
median98
Q3131
95-th percentile239.65
Maximum382
Range377
Interquartile range (IQR)62

Descriptive statistics

Standard deviation77.293475
Coefficient of variation (CV)0.68344603
Kurtosis3.6361944
Mean113.09375
Median Absolute Deviation (MAD)29.5
Skewness1.5522414
Sum3619
Variance5974.2812
MonotonicityNot monotonic
2024-03-15T03:00:13.404258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
69 2
 
6.2%
382 1
 
3.1%
121 1
 
3.1%
5 1
 
3.1%
34 1
 
3.1%
74 1
 
3.1%
19 1
 
3.1%
11 1
 
3.1%
128 1
 
3.1%
95 1
 
3.1%
Other values (21) 21
65.6%
ValueCountFrequency (%)
5 1
3.1%
11 1
3.1%
19 1
3.1%
34 1
3.1%
54 1
3.1%
58 1
3.1%
59 1
3.1%
69 2
6.2%
74 1
3.1%
77 1
3.1%
ValueCountFrequency (%)
382 1
3.1%
249 1
3.1%
232 1
3.1%
217 1
3.1%
179 1
3.1%
176 1
3.1%
161 1
3.1%
140 1
3.1%
128 1
3.1%
126 1
3.1%

절도 발생
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1040.0312
Minimum0
Maximum2341
Zeros1
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T03:00:13.797000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile216.3
Q1741.25
median904.5
Q31410
95-th percentile1930.3
Maximum2341
Range2341
Interquartile range (IQR)668.75

Descriptive statistics

Standard deviation569.09155
Coefficient of variation (CV)0.54718697
Kurtosis-0.28511976
Mean1040.0312
Median Absolute Deviation (MAD)327
Skewness0.34186646
Sum33281
Variance323865.19
MonotonicityNot monotonic
2024-03-15T03:00:14.183143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 1
 
3.1%
2341 1
 
3.1%
279 1
 
3.1%
310 1
 
3.1%
629 1
 
3.1%
739 1
 
3.1%
219 1
 
3.1%
213 1
 
3.1%
788 1
 
3.1%
1468 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0 1
3.1%
213 1
3.1%
219 1
3.1%
279 1
3.1%
310 1
3.1%
588 1
3.1%
629 1
3.1%
739 1
3.1%
742 1
3.1%
751 1
3.1%
ValueCountFrequency (%)
2341 1
3.1%
1993 1
3.1%
1879 1
3.1%
1866 1
3.1%
1854 1
3.1%
1642 1
3.1%
1468 1
3.1%
1416 1
3.1%
1408 1
3.1%
1289 1
3.1%

절도 검거
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean630.4375
Minimum2
Maximum1363
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T03:00:14.562358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile126.55
Q1431
median596
Q3877.75
95-th percentile1259.8
Maximum1363
Range1361
Interquartile range (IQR)446.75

Descriptive statistics

Standard deviation344.14147
Coefficient of variation (CV)0.54587722
Kurtosis-0.28913924
Mean630.4375
Median Absolute Deviation (MAD)200.5
Skewness0.31677313
Sum20174
Variance118433.35
MonotonicityNot monotonic
2024-03-15T03:00:14.987731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
2 1
 
3.1%
1258 1
 
3.1%
152 1
 
3.1%
185 1
 
3.1%
371 1
 
3.1%
411 1
 
3.1%
136 1
 
3.1%
115 1
 
3.1%
528 1
 
3.1%
859 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
2 1
3.1%
115 1
3.1%
136 1
3.1%
152 1
3.1%
185 1
3.1%
371 1
3.1%
411 1
3.1%
428 1
3.1%
432 1
3.1%
437 1
3.1%
ValueCountFrequency (%)
1363 1
3.1%
1262 1
3.1%
1258 1
3.1%
1091 1
3.1%
974 1
3.1%
950 1
3.1%
938 1
3.1%
934 1
3.1%
859 1
3.1%
812 1
3.1%

폭력 발생
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1528.25
Minimum0
Maximum3684
Zeros1
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T03:00:15.374403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile265.95
Q11054
median1390
Q32024.25
95-th percentile2825.35
Maximum3684
Range3684
Interquartile range (IQR)970.25

Descriptive statistics

Standard deviation835.24762
Coefficient of variation (CV)0.5465386
Kurtosis0.21486753
Mean1528.25
Median Absolute Deviation (MAD)537
Skewness0.4428379
Sum48904
Variance697638.58
MonotonicityNot monotonic
2024-03-15T03:00:15.791268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 1
 
3.1%
3684 1
 
3.1%
544 1
 
3.1%
549 1
 
3.1%
1152 1
 
3.1%
1245 1
 
3.1%
342 1
 
3.1%
173 1
 
3.1%
1048 1
 
3.1%
2142 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0 1
3.1%
173 1
3.1%
342 1
3.1%
544 1
3.1%
549 1
3.1%
872 1
3.1%
1031 1
3.1%
1048 1
3.1%
1056 1
3.1%
1093 1
3.1%
ValueCountFrequency (%)
3684 1
3.1%
2849 1
3.1%
2806 1
3.1%
2756 1
3.1%
2327 1
3.1%
2291 1
3.1%
2163 1
3.1%
2142 1
3.1%
1985 1
3.1%
1955 1
3.1%

폭력 검거
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1293.3125
Minimum117
Maximum3038
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T03:00:16.173016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum117
5-th percentile232.6
Q1903.5
median1193.5
Q31720.25
95-th percentile2345.7
Maximum3038
Range2921
Interquartile range (IQR)816.75

Descriptive statistics

Standard deviation685.4748
Coefficient of variation (CV)0.53001483
Kurtosis0.074231754
Mean1293.3125
Median Absolute Deviation (MAD)401.5
Skewness0.42316711
Sum41386
Variance469875.71
MonotonicityNot monotonic
2024-03-15T03:00:16.568089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
117 1
 
3.1%
3038 1
 
3.1%
462 1
 
3.1%
468 1
 
3.1%
957 1
 
3.1%
1061 1
 
3.1%
301 1
 
3.1%
149 1
 
3.1%
850 1
 
3.1%
1766 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
117 1
3.1%
149 1
3.1%
301 1
3.1%
462 1
3.1%
468 1
3.1%
781 1
3.1%
850 1
3.1%
881 1
3.1%
911 1
3.1%
935 1
3.1%
ValueCountFrequency (%)
3038 1
3.1%
2349 1
3.1%
2343 1
3.1%
2301 1
3.1%
1957 1
3.1%
1954 1
3.1%
1858 1
3.1%
1766 1
3.1%
1705 1
3.1%
1693 1
3.1%

Interactions

2024-03-15T03:00:03.472754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:39.019576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:41.629611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:44.148479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:47.074449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:49.443867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:51.820930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:55.203465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:57.848386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:00.415093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:03.720974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:39.248183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:41.757253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:44.491769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:47.325344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:49.679670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:52.069026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:55.431404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:58.086872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:00.673065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:03.953883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:39.477107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:41.952971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:44.758519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:47.573554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:49.962507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:52.319357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:55.661286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:58.306271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:00.926759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:04.216813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:39.863415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:42.212153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:45.056336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:47.870653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:50.223631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:52.621675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:55.917781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:58.473386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:01.299349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:04.419577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:40.117486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:42.462640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:45.334634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:48.211055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:50.480377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:53.219868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:56.173463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:58.659570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:01.797047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:04.569337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:40.351382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:42.700524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:45.632992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:48.429437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:50.624668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:53.565693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:56.421522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:58.924622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:02.081714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:04.783035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:40.607579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:42.956881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:45.955527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:48.611422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:50.780741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:53.969738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:56.871072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:59.141537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:02.406301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:04.921075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:40.828051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:43.184267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:46.209971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:48.758016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:50.952926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:54.273969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:57.095787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:59.368556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:02.629650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:05.112167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:41.078511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:43.436790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:46.483013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:48.940444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:51.208169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:54.591378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:57.345158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:59.714214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:02.907930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:05.356794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:41.407247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:43.911115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:46.768078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:49.186814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:51.583658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:54.944385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:59:57.594239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:00.132863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:03.215303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T03:00:16.854458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관서명살인 발생살인 검거강도 발생강도 검거강간 강제추행 발생강간 강제추행 검거절도 발생절도 검거폭력 발생폭력 검거
관서명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
살인 발생1.0001.0000.8900.2460.3780.3840.3610.4930.0000.4330.168
살인 검거1.0000.8901.0000.0000.1340.6100.6520.4760.6540.6220.477
강도 발생1.0000.2460.0001.0000.9210.7190.4740.5900.4020.7450.655
강도 검거1.0000.3780.1340.9211.0000.4980.7330.4670.2760.4150.397
강간 강제추행 발생1.0000.3840.6100.7190.4981.0000.8740.9100.8260.9760.977
강간 강제추행 검거1.0000.3610.6520.4740.7330.8741.0000.9470.7990.8810.898
절도 발생1.0000.4930.4760.5900.4670.9100.9471.0000.9620.9260.945
절도 검거1.0000.0000.6540.4020.2760.8260.7990.9621.0000.8330.855
폭력 발생1.0000.4330.6220.7450.4150.9760.8810.9260.8331.0000.997
폭력 검거1.0000.1680.4770.6550.3970.9770.8980.9450.8550.9971.000
2024-03-15T03:00:17.089083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
살인 발생살인 검거강도 발생강도 검거강간 강제추행 발생강간 강제추행 검거절도 발생절도 검거폭력 발생폭력 검거
살인 발생1.0000.9670.2330.2060.5500.3390.5520.5180.6090.598
살인 검거0.9671.0000.1940.1860.5710.3820.5590.5220.6240.614
강도 발생0.2330.1941.0000.9760.4330.2250.4470.4500.4170.412
강도 검거0.2060.1860.9761.0000.3760.2500.3870.3960.3610.354
강간 강제추행 발생0.5500.5710.4330.3761.0000.8000.9560.8960.9270.930
강간 강제추행 검거0.3390.3820.2250.2500.8001.0000.7670.7280.7300.741
절도 발생0.5520.5590.4470.3870.9560.7671.0000.9520.9620.962
절도 검거0.5180.5220.4500.3960.8960.7280.9521.0000.8830.893
폭력 발생0.6090.6240.4170.3610.9270.7300.9620.8831.0000.996
폭력 검거0.5980.6140.4120.3540.9300.7410.9620.8930.9961.000

Missing values

2024-03-15T03:00:05.570414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T03:00:06.051056image/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

관서명살인 발생살인 검거강도 발생강도 검거강간 강제추행 발생강간 강제추행 검거절도 발생절도 검거폭력 발생폭력 검거
0도경찰청01010382020117
1수원중부6654144121141695016351448
2수원남부67342552321879109128062343
3수원서부2200177140140893418971546
4안양동안2200101977424371031881
5안양만안3333888084963012431081
6군포220086797515531056911
7성남수정216610899113481214431228
8성남중원113212311287642813451159
9분당8644137126104269914351242
관서명살인 발생살인 검거강도 발생강도 검거강간 강제추행 발생강간 강제추행 검거절도 발생절도 검거폭력 발생폭력 검거
22용인서부331168598675111156968
23광주33111059598259021631858
24김포5521136128146885921421766
25하남435677697885281048850
26과천00001411213115173149
27의왕22222619219136342301
28이천5500857473941112451061
29안성556691696293711152957
30여주32114334310185549468
31양평2233505279152544462