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

Description서울특별시 경찰관서별 가정폭력 검거 현황에 대한 내용으로 경찰관서별 검거건수, 검거인원 등의 데이터를 제공합니다.
Author경찰청 서울특별시경찰청
URLhttps://www.data.go.kr/data/3075688/fileData.do

Alerts

신고건수 is highly overall correlated with 검거건수 and 8 other fieldsHigh correlation
검거건수 is highly overall correlated with 신고건수 and 7 other fieldsHigh correlation
검거인원 is highly overall correlated with 신고건수 and 7 other fieldsHigh correlation
기소인원 is highly overall correlated with 신고건수 and 6 other fieldsHigh correlation
구속 is highly overall correlated with 신고건수 and 4 other fieldsHigh correlation
불구속 is highly overall correlated with 신고건수 and 6 other fieldsHigh correlation
불기소 is highly overall correlated with 신고건수 and 7 other fieldsHigh correlation
가정보호사건송치 is highly overall correlated with 신고건수 and 7 other fieldsHigh correlation
기타 is highly overall correlated with 신고건수 and 1 other fieldsHigh correlation
재범인원 is highly overall correlated with 신고건수 and 7 other fieldsHigh correlation
구분 has unique valuesUnique
신고건수 has unique valuesUnique
검거건수 has unique valuesUnique
구속 has 12 (37.5%) zerosZeros
기타 has 3 (9.4%) zerosZeros

Reproduction

Analysis started2024-03-14 19:50:18.505431
Analysis finished2024-03-14 19:50:42.761015
Duration24.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
2024-03-15T04:50:43.451808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.125
Min length4

Characters and Unicode

Total characters132
Distinct characters46
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 row서울청
2nd row서울중부
3rd row서울종로
4th row서울남대문
5th row서울서대문
ValueCountFrequency (%)
서울청 1
 
3.1%
서울중부 1
 
3.1%
서울도봉 1
 
3.1%
서울은평 1
 
3.1%
서울방배 1
 
3.1%
서울노원 1
 
3.1%
서울송파 1
 
3.1%
서울양천 1
 
3.1%
서울서초 1
 
3.1%
서울구로 1
 
3.1%
Other values (22) 22
68.8%
2024-03-15T04:50:44.450826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
서 37
28.0%
울 32
24.2%
강 4
 
3.0%
동 4
 
3.0%
대 3
 
2.3%
문 3
 
2.3%
천 2
 
1.5%
포 2
 
1.5%
북 2
 
1.5%
성 2
 
1.5%
Other values (36) 41
31.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 131
99.2%
Space Separator 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 37
28.2%
울 32
24.4%
강 4
 
3.1%
동 4
 
3.1%
대 3
 
2.3%
문 3
 
2.3%
천 2
 
1.5%
포 2
 
1.5%
북 2
 
1.5%
성 2
 
1.5%
Other values (35) 40
30.5%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 131
99.2%
Common 1
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 37
28.2%
울 32
24.4%
강 4
 
3.1%
동 4
 
3.1%
대 3
 
2.3%
문 3
 
2.3%
천 2
 
1.5%
포 2
 
1.5%
북 2
 
1.5%
성 2
 
1.5%
Other values (35) 40
30.5%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 131
99.2%
ASCII 1
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
서 37
28.2%
울 32
24.4%
강 4
 
3.1%
동 4
 
3.1%
대 3
 
2.3%
문 3
 
2.3%
천 2
 
1.5%
포 2
 
1.5%
북 2
 
1.5%
성 2
 
1.5%
Other values (35) 40
30.5%
ASCII
ValueCountFrequency (%)
1
100.0%

신고건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1213.3125
Minimum87
Maximum2566
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T04:50:44.673367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum87
5-th percentile208.65
Q1798.5
median1201
Q31615
95-th percentile2351.5
Maximum2566
Range2479
Interquartile range (IQR)816.5

Descriptive statistics

Standard deviation626.199
Coefficient of variation (CV)0.51610694
Kurtosis-0.22639965
Mean1213.3125
Median Absolute Deviation (MAD)404
Skewness0.25664528
Sum38826
Variance392125.19
MonotonicityNot monotonic
2024-03-15T04:50:45.011089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1598 1
 
3.1%
1956 1
 
3.1%
1261 1
 
3.1%
1666 1
 
3.1%
1001 1
 
3.1%
464 1
 
3.1%
2338 1
 
3.1%
2566 1
 
3.1%
1560 1
 
3.1%
1017 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
87 1
3.1%
152 1
3.1%
255 1
3.1%
429 1
3.1%
464 1
3.1%
721 1
3.1%
750 1
3.1%
794 1
3.1%
800 1
3.1%
857 1
3.1%
ValueCountFrequency (%)
2566 1
3.1%
2368 1
3.1%
2338 1
3.1%
1956 1
3.1%
1943 1
3.1%
1727 1
3.1%
1702 1
3.1%
1666 1
3.1%
1598 1
3.1%
1560 1
3.1%

검거건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246.8125
Minimum21
Maximum618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T04:50:45.385940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile45.55
Q1162.75
median218
Q3353.75
95-th percentile546.5
Maximum618
Range597
Interquartile range (IQR)191

Descriptive statistics

Standard deviation149.50659
Coefficient of variation (CV)0.60574969
Kurtosis0.32216979
Mean246.8125
Median Absolute Deviation (MAD)79
Skewness0.75844678
Sum7898
Variance22352.222
MonotonicityNot monotonic
2024-03-15T04:50:45.805130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
46 1
 
3.1%
357 1
 
3.1%
205 1
 
3.1%
353 1
 
3.1%
190 1
 
3.1%
69 1
 
3.1%
618 1
 
3.1%
542 1
 
3.1%
433 1
 
3.1%
179 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
21 1
3.1%
45 1
3.1%
46 1
3.1%
47 1
3.1%
69 1
3.1%
124 1
3.1%
154 1
3.1%
159 1
3.1%
164 1
3.1%
179 1
3.1%
ValueCountFrequency (%)
618 1
3.1%
552 1
3.1%
542 1
3.1%
433 1
3.1%
376 1
3.1%
367 1
3.1%
357 1
3.1%
356 1
3.1%
353 1
3.1%
340 1
3.1%

검거인원
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean295.90625
Minimum25
Maximum710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T04:50:46.131376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile49.65
Q1200.25
median263.5
Q3415.5
95-th percentile664.35
Maximum710
Range685
Interquartile range (IQR)215.25

Descriptive statistics

Standard deviation176.85156
Coefficient of variation (CV)0.59766078
Kurtosis0.25612727
Mean295.90625
Median Absolute Deviation (MAD)92.5
Skewness0.70897016
Sum9469
Variance31276.475
MonotonicityNot monotonic
2024-03-15T04:50:46.531830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
265 2
 
6.2%
48 1
 
3.1%
184 1
 
3.1%
262 1
 
3.1%
428 1
 
3.1%
204 1
 
3.1%
86 1
 
3.1%
710 1
 
3.1%
666 1
 
3.1%
512 1
 
3.1%
Other values (21) 21
65.6%
ValueCountFrequency (%)
25 1
3.1%
48 1
3.1%
51 1
3.1%
57 1
3.1%
86 1
3.1%
158 1
3.1%
184 1
3.1%
192 1
3.1%
203 1
3.1%
204 1
3.1%
ValueCountFrequency (%)
710 1
3.1%
666 1
3.1%
663 1
3.1%
512 1
3.1%
450 1
3.1%
443 1
3.1%
428 1
3.1%
426 1
3.1%
412 1
3.1%
380 1
3.1%

기소인원
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.53125
Minimum10
Maximum329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T04:50:46.922780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14.3
Q162.25
median77
Q3119
95-th percentile176.95
Maximum329
Range319
Interquartile range (IQR)56.75

Descriptive statistics

Standard deviation62.857742
Coefficient of variation (CV)0.69432094
Kurtosis5.6192548
Mean90.53125
Median Absolute Deviation (MAD)29
Skewness1.7744243
Sum2897
Variance3951.0958
MonotonicityNot monotonic
2024-03-15T04:50:47.248533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
96 2
 
6.2%
68 2
 
6.2%
10 1
 
3.1%
76 1
 
3.1%
57 1
 
3.1%
149 1
 
3.1%
22 1
 
3.1%
19 1
 
3.1%
329 1
 
3.1%
122 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
10 1
3.1%
11 1
3.1%
17 1
3.1%
19 1
3.1%
22 1
3.1%
23 1
3.1%
53 1
3.1%
57 1
3.1%
64 1
3.1%
67 1
3.1%
ValueCountFrequency (%)
329 1
3.1%
183 1
3.1%
172 1
3.1%
149 1
3.1%
144 1
3.1%
128 1
3.1%
126 1
3.1%
122 1
3.1%
118 1
3.1%
108 1
3.1%

구속
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.96875
Minimum0
Maximum10
Zeros12
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T04:50:47.460149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4426899
Coefficient of variation (CV)1.2407314
Kurtosis2.4699556
Mean1.96875
Median Absolute Deviation (MAD)1
Skewness1.5683883
Sum63
Variance5.9667339
MonotonicityNot monotonic
2024-03-15T04:50:47.653309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 12
37.5%
1 6
18.8%
2 5
15.6%
6 3
 
9.4%
4 2
 
6.2%
3 2
 
6.2%
5 1
 
3.1%
10 1
 
3.1%
ValueCountFrequency (%)
0 12
37.5%
1 6
18.8%
2 5
15.6%
3 2
 
6.2%
4 2
 
6.2%
5 1
 
3.1%
6 3
 
9.4%
10 1
 
3.1%
ValueCountFrequency (%)
10 1
 
3.1%
6 3
 
9.4%
5 1
 
3.1%
4 2
 
6.2%
3 2
 
6.2%
2 5
15.6%
1 6
18.8%
0 12
37.5%

불구속
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.5625
Minimum9
Maximum319
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T04:50:48.004940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile14.3
Q162.25
median74
Q3113.25
95-th percentile176.5
Maximum319
Range310
Interquartile range (IQR)51

Descriptive statistics

Standard deviation61.341423
Coefficient of variation (CV)0.69263427
Kurtosis5.357038
Mean88.5625
Median Absolute Deviation (MAD)30
Skewness1.7430741
Sum2834
Variance3762.7702
MonotonicityNot monotonic
2024-03-15T04:50:48.406667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
70 2
 
6.2%
9 1
 
3.1%
125 1
 
3.1%
57 1
 
3.1%
145 1
 
3.1%
22 1
 
3.1%
19 1
 
3.1%
319 1
 
3.1%
117 1
 
3.1%
107 1
 
3.1%
Other values (21) 21
65.6%
ValueCountFrequency (%)
9 1
3.1%
11 1
3.1%
17 1
3.1%
19 1
3.1%
22 1
3.1%
23 1
3.1%
52 1
3.1%
57 1
3.1%
64 1
3.1%
65 1
3.1%
ValueCountFrequency (%)
319 1
3.1%
182 1
3.1%
172 1
3.1%
145 1
3.1%
140 1
3.1%
128 1
3.1%
125 1
3.1%
117 1
3.1%
112 1
3.1%
107 1
3.1%

불기소
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.03125
Minimum3
Maximum208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T04:50:48.798669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9
Q128.75
median50
Q383.5
95-th percentile187
Maximum208
Range205
Interquartile range (IQR)54.75

Descriptive statistics

Standard deviation51.557349
Coefficient of variation (CV)0.81796489
Kurtosis2.2607773
Mean63.03125
Median Absolute Deviation (MAD)27
Skewness1.5275606
Sum2017
Variance2658.1603
MonotonicityNot monotonic
2024-03-15T04:50:49.176058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
9 2
 
6.2%
29 2
 
6.2%
72 2
 
6.2%
87 1
 
3.1%
45 1
 
3.1%
83 1
 
3.1%
33 1
 
3.1%
17 1
 
3.1%
198 1
 
3.1%
178 1
 
3.1%
Other values (19) 19
59.4%
ValueCountFrequency (%)
3 1
3.1%
9 2
6.2%
15 1
3.1%
17 1
3.1%
18 1
3.1%
27 1
3.1%
28 1
3.1%
29 2
6.2%
31 1
3.1%
33 1
3.1%
ValueCountFrequency (%)
208 1
3.1%
198 1
3.1%
178 1
3.1%
102 1
3.1%
98 1
3.1%
89 1
3.1%
87 1
3.1%
85 1
3.1%
83 1
3.1%
81 1
3.1%

가정보호사건송치
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.15625
Minimum3
Maximum318
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T04:50:49.490729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile14.85
Q172.25
median126
Q3182.25
95-th percentile294.65
Maximum318
Range315
Interquartile range (IQR)110

Descriptive statistics

Standard deviation82.996739
Coefficient of variation (CV)0.62801978
Kurtosis-0.048726477
Mean132.15625
Median Absolute Deviation (MAD)57
Skewness0.58644469
Sum4229
Variance6888.4587
MonotonicityNot monotonic
2024-03-15T04:50:49.707719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
195 2
 
6.2%
94 2
 
6.2%
126 2
 
6.2%
3 1
 
3.1%
154 1
 
3.1%
143 1
 
3.1%
47 1
 
3.1%
175 1
 
3.1%
315 1
 
3.1%
240 1
 
3.1%
Other values (19) 19
59.4%
ValueCountFrequency (%)
3 1
3.1%
11 1
3.1%
18 1
3.1%
25 1
3.1%
47 1
3.1%
56 1
3.1%
62 1
3.1%
64 1
3.1%
75 1
3.1%
94 2
6.2%
ValueCountFrequency (%)
318 1
3.1%
315 1
3.1%
278 1
3.1%
240 1
3.1%
212 1
3.1%
208 1
3.1%
195 2
6.2%
178 1
3.1%
175 1
3.1%
154 1
3.1%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.1875
Minimum0
Maximum62
Zeros3
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T04:50:50.051533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q312.25
95-th percentile37.25
Maximum62
Range62
Interquartile range (IQR)10.25

Descriptive statistics

Standard deviation14.019428
Coefficient of variation (CV)1.3761401
Kurtosis7.0217002
Mean10.1875
Median Absolute Deviation (MAD)5
Skewness2.5751581
Sum326
Variance196.54435
MonotonicityNot monotonic
2024-03-15T04:50:50.586147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 4
12.5%
0 3
 
9.4%
6 3
 
9.4%
2 3
 
9.4%
26 2
 
6.2%
8 2
 
6.2%
13 2
 
6.2%
3 2
 
6.2%
4 2
 
6.2%
19 1
 
3.1%
Other values (8) 8
25.0%
ValueCountFrequency (%)
0 3
9.4%
1 4
12.5%
2 3
9.4%
3 2
6.2%
4 2
6.2%
5 1
 
3.1%
6 3
9.4%
7 1
 
3.1%
8 2
6.2%
9 1
 
3.1%
ValueCountFrequency (%)
62 1
3.1%
51 1
3.1%
26 2
6.2%
19 1
3.1%
14 1
3.1%
13 2
6.2%
12 1
3.1%
11 1
3.1%
9 1
3.1%
8 2
6.2%

재범인원
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.8125
Minimum2
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T04:50:50.803960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.75
Q119.5
median29
Q352.75
95-th percentile63.35
Maximum83
Range81
Interquartile range (IQR)33.25

Descriptive statistics

Standard deviation20.522902
Coefficient of variation (CV)0.58952681
Kurtosis-0.60022179
Mean34.8125
Median Absolute Deviation (MAD)14
Skewness0.30755345
Sum1114
Variance421.18952
MonotonicityNot monotonic
2024-03-15T04:50:51.047125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
28 3
 
9.4%
2 2
 
6.2%
29 2
 
6.2%
57 2
 
6.2%
59 1
 
3.1%
16 1
 
3.1%
83 1
 
3.1%
49 1
 
3.1%
7 1
 
3.1%
62 1
 
3.1%
Other values (17) 17
53.1%
ValueCountFrequency (%)
2 2
6.2%
7 1
3.1%
8 1
3.1%
12 1
3.1%
16 1
3.1%
17 1
3.1%
18 1
3.1%
20 1
3.1%
23 1
3.1%
24 1
3.1%
ValueCountFrequency (%)
83 1
3.1%
65 1
3.1%
62 1
3.1%
59 1
3.1%
58 1
3.1%
57 2
6.2%
55 1
3.1%
52 1
3.1%
49 1
3.1%
44 1
3.1%

Interactions

2024-03-15T04:50:40.453071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:19.152374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:21.823235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:24.339701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:26.388471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:28.780811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:30.894177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:33.301249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:35.968149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:38.223701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:40.603363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:19.400029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:22.057426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:24.585447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:26.607852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:29.076518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:31.132548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:33.544091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:36.274126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:38.365079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:40.777277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:19.690925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:22.371303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:24.837049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:26.916455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:29.272453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:31.379475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:33.831298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:36.511383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:38.603442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:41.036546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:20.069020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:22.632023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:25.094944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:27.381033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:29.420510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:31.637600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:34.120034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:36.675962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:38.769678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:41.210481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:20.331080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:22.899135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:25.346825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:27.627184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:29.620725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:31.882776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:34.400125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:37.005895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:38.926300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:41.364480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:20.589933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:23.154066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:25.603710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:27.825313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:29.809878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:32.114058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:34.669925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:37.246417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:39.217675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:41.518552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:20.882367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:23.346208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:25.765029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:28.089729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:29.994265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:32.423254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:34.950290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:37.498097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:39.575836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:41.662233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:21.135159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:23.572323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:25.904083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:28.274679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:30.228343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:32.680334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:35.197099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:37.731997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:39.795067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:41.830230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:21.401699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:23.824794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:26.058203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:28.434042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:30.475869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:32.860785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:35.453922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:37.891597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:40.044227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:42.028471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:21.651434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:24.072845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:26.224310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:28.591865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:30.727503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:33.018636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:35.708353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:38.039335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:50:40.291105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T04:50:51.291523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분신고건수검거건수검거인원기소인원구속불구속불기소가정보호사건송치기타재범인원
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
신고건수1.0001.0000.9370.9320.5180.4460.5170.6320.8110.4460.826
검거건수1.0000.9371.0000.9860.8200.7320.8120.7660.8910.6920.894
검거인원1.0000.9320.9861.0000.6860.4030.6790.7200.8710.6440.885
기소인원1.0000.5180.8200.6861.0000.8031.0000.7960.7100.3500.700
구속1.0000.4460.7320.4030.8031.0000.7910.6860.5700.6820.591
불구속1.0000.5170.8120.6791.0000.7911.0000.7900.7000.4370.683
불기소1.0000.6320.7660.7200.7960.6860.7901.0000.6760.8430.756
가정보호사건송치1.0000.8110.8910.8710.7100.5700.7000.6761.0000.7390.603
기타1.0000.4460.6920.6440.3500.6820.4370.8430.7391.0000.097
재범인원1.0000.8260.8940.8850.7000.5910.6830.7560.6030.0971.000
2024-03-15T04:50:51.629434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신고건수검거건수검거인원기소인원구속불구속불기소가정보호사건송치기타재범인원
신고건수1.0000.8640.8510.6990.5710.6940.6990.7620.6070.716
검거건수0.8641.0000.9900.8200.5920.8160.8270.9160.4990.867
검거인원0.8510.9901.0000.8490.6020.8440.8250.9090.4660.866
기소인원0.6990.8200.8491.0000.4940.9990.5990.6440.2650.749
구속0.5710.5920.6020.4941.0000.4820.5820.4710.3510.563
불구속0.6940.8160.8440.9990.4821.0000.5900.6400.2730.748
불기소0.6990.8270.8250.5990.5820.5901.0000.7680.3660.699
가정보호사건송치0.7620.9160.9090.6440.4710.6400.7681.0000.5050.871
기타0.6070.4990.4660.2650.3510.2730.3660.5051.0000.454
재범인원0.7160.8670.8660.7490.5630.7480.6990.8710.4541.000

Missing values

2024-03-15T04:50:42.349398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T04:50:42.618946image/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

구분신고건수검거건수검거인원기소인원구속불구속불기소가정보호사건송치기타재범인원
0서울청15984648101993262
1서울중부429124158672652762224
2서울종로25545572302392508
3서울남대문8721251101131102
4서울서대문9632162659629434127828
5서울혜화1524751170171518112
6서울용산7501912286406429130529
7서울성북8001641926806818941229
8서울동대문130722926917201723164227
9서울마포122022525972270721021323
구분신고건수검거건수검거인원기소인원구속불구속불기소가정보호사건송치기타재범인원
22서울종암857159203531527275320
23서울구로1702356426128012881208958
24서울서초1017179220692675594217
25서울양천156043351210811071022406252
26서울송파256654266612251171783155165
27서울노원233861871032910319198175862
28서울방배464698619019174737
29서울은평10011902042202233143649
30서울도봉1666353428149414583195183
31서울수서12612052625705745154616