Overview

Dataset statistics

Number of variables4
Number of observations310
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.1 KiB
Average record size in memory33.4 B

Variable types

Categorical3
Numeric1

Dataset

Description2022년 서울경찰청 관할 경찰서별 살인, 강도, 강간 및 추행, 절도, 폭력 발생 검거 현황 (2022년 관서별 5대범죄 발생 검거 현황)
Author경찰청 서울특별시경찰청
URLhttps://www.data.go.kr/data/15054738/fileData.do

Alerts

건수 has 13 (4.2%) zerosZeros

Reproduction

Analysis started2024-03-14 14:31:25.005297
Analysis finished2024-03-14 14:31:25.943447
Duration0.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct31
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
중부
 
10
종로
 
10
남대문
 
10
서대문
 
10
혜화
 
10
Other values (26)
260 

Length

Max length3
Median length2
Mean length2.1290323
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중부 10
 
3.2%
종로 10
 
3.2%
남대문 10
 
3.2%
서대문 10
 
3.2%
혜화 10
 
3.2%
용산 10
 
3.2%
성북 10
 
3.2%
동대문 10
 
3.2%
마포 10
 
3.2%
영등포 10
 
3.2%
Other values (21) 210
67.7%

Length

2024-03-14T23:31:26.162220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중부 10
 
3.2%
중랑 10
 
3.2%
도봉 10
 
3.2%
은평 10
 
3.2%
방배 10
 
3.2%
노원 10
 
3.2%
송파 10
 
3.2%
양천 10
 
3.2%
서초 10
 
3.2%
구로 10
 
3.2%
Other values (21) 210
67.7%

죄종
Categorical

Distinct6
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
살인
62 
강도
62 
절도
62 
폭력
62 
강간,추행
52 

Length

Max length5
Median length2
Mean length2.5032258
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row살인
2nd row살인
3rd row강도
4th row강도
5th row강간,추행

Common Values

ValueCountFrequency (%)
살인 62
20.0%
강도 62
20.0%
절도 62
20.0%
폭력 62
20.0%
강간,추행 52
16.8%
강간 10
 
3.2%

Length

2024-03-14T23:31:26.572379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:31:26.922666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
살인 62
20.0%
강도 62
20.0%
절도 62
20.0%
폭력 62
20.0%
강간,추행 52
16.8%
강간 10
 
3.2%

발생검거
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
발생
155 
검거
155 

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 155
50.0%
검거 155
50.0%

Length

2024-03-14T23:31:27.315048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:31:27.633265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
발생 155
50.0%
검거 155
50.0%

건수
Real number (ℝ)

ZEROS 

Distinct189
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean497.85484
Minimum0
Maximum2669
Zeros13
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-14T23:31:27.866232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median143
Q3840.5
95-th percentile1974.25
Maximum2669
Range2669
Interquartile range (IQR)837.5

Descriptive statistics

Standard deviation663.48723
Coefficient of variation (CV)1.3326921
Kurtosis0.79406668
Mean497.85484
Median Absolute Deviation (MAD)141
Skewness1.3424788
Sum154335
Variance440215.3
MonotonicityNot monotonic
2024-03-14T23:31:28.124029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 36
 
11.6%
3 19
 
6.1%
5 15
 
4.8%
0 13
 
4.2%
1 11
 
3.5%
6 9
 
2.9%
4 5
 
1.6%
7 4
 
1.3%
9 4
 
1.3%
8 4
 
1.3%
Other values (179) 190
61.3%
ValueCountFrequency (%)
0 13
 
4.2%
1 11
 
3.5%
2 36
11.6%
3 19
6.1%
4 5
 
1.6%
5 15
4.8%
6 9
 
2.9%
7 4
 
1.3%
8 4
 
1.3%
9 4
 
1.3%
ValueCountFrequency (%)
2669 1
0.3%
2567 1
0.3%
2494 1
0.3%
2415 1
0.3%
2401 1
0.3%
2206 1
0.3%
2205 1
0.3%
2201 2
0.6%
2178 1
0.3%
2148 1
0.3%

Interactions

2024-03-14T23:31:25.220746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T23:31:28.324385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분죄종발생검거건수
구분1.0000.0000.0000.288
죄종0.0001.0000.0000.683
발생검거0.0000.0001.0000.148
건수0.2880.6830.1481.000
2024-03-14T23:31:28.577307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분발생검거죄종
구분1.0000.0000.000
발생검거0.0001.0000.000
죄종0.0000.0001.000
2024-03-14T23:31:28.845041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건수구분죄종발생검거
건수1.0000.1000.4440.112
구분0.1001.0000.0000.000
죄종0.4440.0001.0000.000
발생검거0.1120.0000.0001.000

Missing values

2024-03-14T23:31:25.557667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T23:31:25.833866image/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

구분죄종발생검거건수
0중부살인발생1
1중부살인검거2
2중부강도발생3
3중부강도검거2
4중부강간,추행발생137
5중부강간,추행검거87
6중부절도발생910
7중부절도검거459
8중부폭력발생1026
9중부폭력검거839
구분죄종발생검거건수
300수서살인발생6
301수서살인검거5
302수서강도발생2
303수서강도검거2
304수서강간,추행발생209
305수서강간,추행검거175
306수서절도발생930
307수서절도검거553
308수서폭력발생1268
309수서폭력검거1081