Overview

Dataset statistics

Number of variables13
Number of observations38
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 KiB
Average record size in memory118.5 B

Variable types

Categorical3
Text1
Numeric9

Dataset

Description범죄유형(강력범죄, 절도범죄, 폭력범죄, 지능범죄, 풍속범죄), 검거자 유형(수사-형사, 외근-112차, 교통, 검문소, 기타경찰 등)별로 데이터 건수 제공
Author경찰청
URLhttps://www.data.go.kr/data/3074455/fileData.do

Alerts

수사_형사 is highly overall correlated with 외근_112차 and 10 other fieldsHigh correlation
외근_112차 is highly overall correlated with 수사_형사 and 10 other fieldsHigh correlation
교통 is highly overall correlated with 수사_형사 and 9 other fieldsHigh correlation
기타 경찰 is highly overall correlated with 수사_형사 and 9 other fieldsHigh correlation
경비원 is highly overall correlated with 수사_형사 and 10 other fieldsHigh correlation
피해자 is highly overall correlated with 수사_형사 and 10 other fieldsHigh correlation
기타 민간인 is highly overall correlated with 수사_형사 and 10 other fieldsHigh correlation
기타 is highly overall correlated with 수사_형사 and 9 other fieldsHigh correlation
미상 is highly overall correlated with 수사_형사 and 9 other fieldsHigh correlation
범죄대분류 is highly overall correlated with 수사_형사 and 8 other fieldsHigh correlation
검문소 is highly overall correlated with 수사_형사 and 9 other fieldsHigh correlation
방범원 is highly overall correlated with 수사_형사 and 7 other fieldsHigh correlation
검문소 is highly imbalanced (70.3%)Imbalance
범죄중분류 has unique valuesUnique
수사_형사 has unique valuesUnique
미상 has unique valuesUnique
외근_112차 has 1 (2.6%) zerosZeros
교통 has 11 (28.9%) zerosZeros
경비원 has 22 (57.9%) zerosZeros
피해자 has 12 (31.6%) zerosZeros
기타 민간인 has 11 (28.9%) zerosZeros
기타 has 3 (7.9%) zerosZeros

Reproduction

Analysis started2023-12-12 10:34:03.565867
Analysis finished2023-12-12 10:34:15.156625
Duration11.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄대분류
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Memory size436.0 B
지능범죄
9 
강력범죄
8 
폭력범죄
8 
풍속범죄
2 
절도범죄
 
1
Other values (10)
10 

Length

Max length6
Median length4
Mean length4.0526316
Min length4

Unique

Unique11 ?
Unique (%)28.9%

Sample

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

Common Values

ValueCountFrequency (%)
지능범죄 9
23.7%
강력범죄 8
21.1%
폭력범죄 8
21.1%
풍속범죄 2
 
5.3%
절도범죄 1
 
2.6%
특별경제범죄 1
 
2.6%
마약범죄 1
 
2.6%
보건범죄 1
 
2.6%
환경범죄 1
 
2.6%
교통범죄 1
 
2.6%
Other values (5) 5
13.2%

Length

2023-12-12T19:34:15.257127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지능범죄 9
23.7%
강력범죄 8
21.1%
폭력범죄 8
21.1%
풍속범죄 2
 
5.3%
절도범죄 1
 
2.6%
특별경제범죄 1
 
2.6%
마약범죄 1
 
2.6%
보건범죄 1
 
2.6%
환경범죄 1
 
2.6%
교통범죄 1
 
2.6%
Other values (5) 5
13.2%

범죄중분류
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-12T19:34:15.533904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length6
Mean length3.7894737
Min length2

Characters and Unicode

Total characters144
Distinct characters73
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

Unique38 ?
Unique (%)100.0%

Sample

1st row살인기수
2nd row살인미수등
3rd row강도
4th row강간
5th row유사강간
ValueCountFrequency (%)
강간 2
 
4.7%
살인기수 1
 
2.3%
도박범죄 1
 
2.3%
병역범죄 1
 
2.3%
문서 1
 
2.3%
인장 1
 
2.3%
유가증권인지 1
 
2.3%
사기 1
 
2.3%
횡령 1
 
2.3%
배임 1
 
2.3%
Other values (32) 32
74.4%
2023-12-12T19:34:15.985469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
범 13
 
9.0%
죄 13
 
9.0%
강 6
 
4.2%
기 5
 
3.5%
5
 
3.5%
인 5
 
3.5%
유 4
 
2.8%
행 4
 
2.8%
제 3
 
2.1%
간 3
 
2.1%
Other values (63) 83
57.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 139
96.5%
Space Separator 5
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
범 13
 
9.4%
죄 13
 
9.4%
강 6
 
4.3%
기 5
 
3.6%
인 5
 
3.6%
유 4
 
2.9%
행 4
 
2.9%
제 3
 
2.2%
간 3
 
2.2%
등 3
 
2.2%
Other values (62) 80
57.6%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 139
96.5%
Common 5
 
3.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
범 13
 
9.4%
죄 13
 
9.4%
강 6
 
4.3%
기 5
 
3.6%
인 5
 
3.6%
유 4
 
2.9%
행 4
 
2.9%
제 3
 
2.2%
간 3
 
2.2%
등 3
 
2.2%
Other values (62) 80
57.6%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 139
96.5%
ASCII 5
 
3.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
범 13
 
9.4%
죄 13
 
9.4%
강 6
 
4.3%
기 5
 
3.6%
인 5
 
3.6%
유 4
 
2.9%
행 4
 
2.9%
제 3
 
2.2%
간 3
 
2.2%
등 3
 
2.2%
Other values (62) 80
57.6%
ASCII
ValueCountFrequency (%)
5
100.0%

수사_형사
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16261.395
Minimum17
Maximum129770
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:34:16.186520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile96.45
Q1537.5
median3105
Q313537.75
95-th percentile80219.8
Maximum129770
Range129753
Interquartile range (IQR)13000.25

Descriptive statistics

Standard deviation30501.083
Coefficient of variation (CV)1.8756745
Kurtosis6.2431582
Mean16261.395
Median Absolute Deviation (MAD)2979
Skewness2.5670963
Sum617933
Variance9.3031607 × 108
MonotonicityNot monotonic
2023-12-12T19:34:16.404010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
168 1
 
2.6%
10523 1
 
2.6%
31 1
 
2.6%
106750 1
 
2.6%
20623 1
 
2.6%
2752 1
 
2.6%
8012 1
 
2.6%
7446 1
 
2.6%
30652 1
 
2.6%
43958 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
17 1
2.6%
31 1
2.6%
108 1
2.6%
144 1
2.6%
145 1
2.6%
168 1
2.6%
240 1
2.6%
244 1
2.6%
296 1
2.6%
533 1
2.6%
ValueCountFrequency (%)
129770 1
2.6%
106750 1
2.6%
75538 1
2.6%
74465 1
2.6%
43958 1
2.6%
30652 1
2.6%
22281 1
2.6%
20765 1
2.6%
20623 1
2.6%
14371 1
2.6%

외근_112차
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5528.8947
Minimum0
Maximum87796
Zeros1
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:34:16.601346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q131
median290.5
Q33684.25
95-th percentile27859.25
Maximum87796
Range87796
Interquartile range (IQR)3653.25

Descriptive statistics

Standard deviation15336.946
Coefficient of variation (CV)2.7739624
Kurtosis23.480705
Mean5528.8947
Median Absolute Deviation (MAD)289
Skewness4.6016745
Sum210098
Variance2.3522192 × 108
MonotonicityNot monotonic
2023-12-12T19:34:16.778883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1 3
 
7.9%
4 2
 
5.3%
229 1
 
2.6%
318 1
 
2.6%
6078 1
 
2.6%
1054 1
 
2.6%
20 1
 
2.6%
1942 1
 
2.6%
3145 1
 
2.6%
8124 1
 
2.6%
Other values (25) 25
65.8%
ValueCountFrequency (%)
0 1
 
2.6%
1 3
7.9%
2 1
 
2.6%
4 2
5.3%
20 1
 
2.6%
22 1
 
2.6%
28 1
 
2.6%
40 1
 
2.6%
52 1
 
2.6%
67 1
 
2.6%
ValueCountFrequency (%)
87796 1
2.6%
32122 1
2.6%
27107 1
2.6%
9945 1
2.6%
8124 1
2.6%
7560 1
2.6%
7158 1
2.6%
6078 1
2.6%
5815 1
2.6%
3864 1
2.6%

교통
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3098.5789
Minimum0
Maximum111332
Zeros11
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:34:17.320760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.5
Q322.25
95-th percentile1942.5
Maximum111332
Range111332
Interquartile range (IQR)22.25

Descriptive statistics

Standard deviation18043.216
Coefficient of variation (CV)5.8230615
Kurtosis37.900493
Mean3098.5789
Median Absolute Deviation (MAD)3.5
Skewness6.152824
Sum117746
Variance3.2555764 × 108
MonotonicityNot monotonic
2023-12-12T19:34:17.510679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 11
28.9%
4 4
 
10.5%
1 3
 
7.9%
2 3
 
7.9%
3 2
 
5.3%
23 1
 
2.6%
3515 1
 
2.6%
111332 1
 
2.6%
8 1
 
2.6%
20 1
 
2.6%
Other values (10) 10
26.3%
ValueCountFrequency (%)
0 11
28.9%
1 3
 
7.9%
2 3
 
7.9%
3 2
 
5.3%
4 4
 
10.5%
5 1
 
2.6%
6 1
 
2.6%
7 1
 
2.6%
8 1
 
2.6%
20 1
 
2.6%
ValueCountFrequency (%)
111332 1
2.6%
3515 1
2.6%
1665 1
2.6%
701 1
2.6%
143 1
2.6%
107 1
2.6%
78 1
2.6%
62 1
2.6%
43 1
2.6%
23 1
2.6%

검문소
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size436.0 B
0
35 
1
 
2
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 35
92.1%
1 2
 
5.3%
6 1
 
2.6%

Length

2023-12-12T19:34:17.688810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:34:17.851857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 35
92.1%
1 2
 
5.3%
6 1
 
2.6%

기타 경찰
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2705.7368
Minimum2
Maximum26939
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:34:18.018988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.85
Q136.75
median348.5
Q32935.5
95-th percentile13468.55
Maximum26939
Range26937
Interquartile range (IQR)2898.75

Descriptive statistics

Standard deviation5528.1345
Coefficient of variation (CV)2.0431161
Kurtosis11.006357
Mean2705.7368
Median Absolute Deviation (MAD)339
Skewness3.1953819
Sum102818
Variance30560271
MonotonicityNot monotonic
2023-12-12T19:34:18.250632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
12 2
 
5.3%
6 1
 
2.6%
536 1
 
2.6%
5 1
 
2.6%
6104 1
 
2.6%
1219 1
 
2.6%
166 1
 
2.6%
6793 1
 
2.6%
322 1
 
2.6%
1379 1
 
2.6%
Other values (27) 27
71.1%
ValueCountFrequency (%)
2 1
2.6%
5 1
2.6%
6 1
2.6%
7 1
2.6%
12 2
5.3%
18 1
2.6%
20 1
2.6%
28 1
2.6%
30 1
2.6%
57 1
2.6%
ValueCountFrequency (%)
26939 1
2.6%
18566 1
2.6%
12569 1
2.6%
6793 1
2.6%
6104 1
2.6%
5762 1
2.6%
3624 1
2.6%
3301 1
2.6%
3137 1
2.6%
2990 1
2.6%

방범원
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size436.0 B
0
28 
2
5 
1
4 
7
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28
73.7%
2 5
 
13.2%
1 4
 
10.5%
7 1
 
2.6%

Length

2023-12-12T19:34:18.451539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:34:18.640926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
73.7%
2 5
 
13.2%
1 4
 
10.5%
7 1
 
2.6%

경비원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9473684
Minimum0
Maximum61
Zeros22
Zeros (%)57.9%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:34:18.798911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile8.65
Maximum61
Range61
Interquartile range (IQR)1

Descriptive statistics

Standard deviation10.179331
Coefficient of variation (CV)3.4537015
Kurtosis30.525386
Mean2.9473684
Median Absolute Deviation (MAD)0
Skewness5.3690604
Sum112
Variance103.61878
MonotonicityNot monotonic
2023-12-12T19:34:18.985347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 22
57.9%
1 7
 
18.4%
3 3
 
7.9%
4 2
 
5.3%
61 1
 
2.6%
2 1
 
2.6%
7 1
 
2.6%
18 1
 
2.6%
ValueCountFrequency (%)
0 22
57.9%
1 7
 
18.4%
2 1
 
2.6%
3 3
 
7.9%
4 2
 
5.3%
7 1
 
2.6%
18 1
 
2.6%
61 1
 
2.6%
ValueCountFrequency (%)
61 1
 
2.6%
18 1
 
2.6%
7 1
 
2.6%
4 2
 
5.3%
3 3
 
7.9%
2 1
 
2.6%
1 7
 
18.4%
0 22
57.9%

피해자
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.578947
Minimum0
Maximum922
Zeros12
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:34:19.294930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.5
Q351.75
95-th percentile402.2
Maximum922
Range922
Interquartile range (IQR)51.75

Descriptive statistics

Standard deviation175.63393
Coefficient of variation (CV)2.5242395
Kurtosis15.682509
Mean69.578947
Median Absolute Deviation (MAD)2.5
Skewness3.7657963
Sum2644
Variance30847.277
MonotonicityNot monotonic
2023-12-12T19:34:19.520827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 12
31.6%
2 4
 
10.5%
1 3
 
7.9%
5 2
 
5.3%
10 1
 
2.6%
220 1
 
2.6%
477 1
 
2.6%
39 1
 
2.6%
66 1
 
2.6%
4 1
 
2.6%
Other values (11) 11
28.9%
ValueCountFrequency (%)
0 12
31.6%
1 3
 
7.9%
2 4
 
10.5%
3 1
 
2.6%
4 1
 
2.6%
5 2
 
5.3%
9 1
 
2.6%
10 1
 
2.6%
20 1
 
2.6%
28 1
 
2.6%
ValueCountFrequency (%)
922 1
2.6%
477 1
2.6%
389 1
2.6%
220 1
2.6%
117 1
2.6%
108 1
2.6%
86 1
2.6%
69 1
2.6%
66 1
2.6%
56 1
2.6%

기타 민간인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.5
Minimum0
Maximum335
Zeros11
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:34:19.689349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q310.5
95-th percentile77.55
Maximum335
Range335
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation57.323053
Coefficient of variation (CV)2.9396438
Kurtosis26.142004
Mean19.5
Median Absolute Deviation (MAD)1.5
Skewness4.8742514
Sum741
Variance3285.9324
MonotonicityNot monotonic
2023-12-12T19:34:19.874165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 11
28.9%
1 8
21.1%
2 4
 
10.5%
21 1
 
2.6%
109 1
 
2.6%
62 1
 
2.6%
19 1
 
2.6%
6 1
 
2.6%
72 1
 
2.6%
11 1
 
2.6%
Other values (8) 8
21.1%
ValueCountFrequency (%)
0 11
28.9%
1 8
21.1%
2 4
 
10.5%
3 1
 
2.6%
6 1
 
2.6%
7 1
 
2.6%
8 1
 
2.6%
9 1
 
2.6%
11 1
 
2.6%
12 1
 
2.6%
ValueCountFrequency (%)
335 1
2.6%
109 1
2.6%
72 1
2.6%
62 1
2.6%
37 1
2.6%
21 1
2.6%
19 1
2.6%
14 1
2.6%
12 1
2.6%
11 1
2.6%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean331.21053
Minimum0
Maximum3343
Zeros3
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:34:20.085360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.75
median42
Q3165
95-th percentile1850.6
Maximum3343
Range3343
Interquartile range (IQR)161.25

Descriptive statistics

Standard deviation766.79823
Coefficient of variation (CV)2.3151385
Kurtosis10.122799
Mean331.21053
Median Absolute Deviation (MAD)42
Skewness3.2158443
Sum12586
Variance587979.52
MonotonicityNot monotonic
2023-12-12T19:34:20.284029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 3
 
7.9%
0 3
 
7.9%
2 3
 
7.9%
11 2
 
5.3%
3112 1
 
2.6%
145 1
 
2.6%
23 1
 
2.6%
6 1
 
2.6%
28 1
 
2.6%
3343 1
 
2.6%
Other values (21) 21
55.3%
ValueCountFrequency (%)
0 3
7.9%
1 3
7.9%
2 3
7.9%
3 1
 
2.6%
6 1
 
2.6%
7 1
 
2.6%
11 2
5.3%
12 1
 
2.6%
13 1
 
2.6%
23 1
 
2.6%
ValueCountFrequency (%)
3343 1
2.6%
3112 1
2.6%
1628 1
2.6%
1114 1
2.6%
742 1
2.6%
534 1
2.6%
293 1
2.6%
199 1
2.6%
175 1
2.6%
168 1
2.6%

미상
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7998.4474
Minimum32
Maximum102938
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:34:20.494917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile47.35
Q1175
median1325
Q34961.5
95-th percentile36024
Maximum102938
Range102906
Interquartile range (IQR)4786.5

Descriptive statistics

Standard deviation19971.518
Coefficient of variation (CV)2.4969244
Kurtosis15.66686
Mean7998.4474
Median Absolute Deviation (MAD)1254.5
Skewness3.8588584
Sum303941
Variance3.9886155 × 108
MonotonicityNot monotonic
2023-12-12T19:34:20.746424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
60 1
 
2.6%
910 1
 
2.6%
49 1
 
2.6%
102938 1
 
2.6%
9084 1
 
2.6%
3083 1
 
2.6%
5007 1
 
2.6%
1120 1
 
2.6%
18316 1
 
2.6%
6931 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
32 1
2.6%
38 1
2.6%
49 1
2.6%
60 1
2.6%
62 1
2.6%
79 1
2.6%
86 1
2.6%
96 1
2.6%
114 1
2.6%
174 1
2.6%
ValueCountFrequency (%)
102938 1
2.6%
68188 1
2.6%
30348 1
2.6%
19278 1
2.6%
18316 1
2.6%
9084 1
2.6%
7216 1
2.6%
6931 1
2.6%
5315 1
2.6%
5007 1
2.6%

Interactions

2023-12-12T19:34:13.646856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:04.218618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:05.235065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:06.343258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:07.437435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:09.007872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:10.202271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:11.276968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:12.468764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:13.783583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:04.322372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:05.360574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:06.463346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:07.576054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:09.158662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:10.338322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:11.392652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:12.609894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:13.890825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:04.422194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:05.488519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:06.592219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:07.739211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:09.276308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:10.444707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:11.493454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:12.731413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:14.003613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:04.541846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:05.634325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:06.706849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:07.838558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:09.412252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:10.562674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:11.620710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:12.863751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:14.121949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:04.654191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:05.756837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:06.816167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:07.966709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:09.539553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:10.680826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:11.783892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:12.992711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:14.255231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:04.771904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:05.893936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:06.948752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:08.098780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:09.675006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:10.804298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:11.938678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:13.122608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:14.369478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:04.876850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:06.009652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:07.062185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:08.209409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:09.795212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:10.909071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:12.062678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:13.250383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:14.505251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:04.994510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:06.125996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:07.197683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:08.729500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:09.938585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:11.042981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:12.204323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:13.379103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:14.639335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:05.120029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:06.242977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:07.320598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:08.864707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:10.073610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:11.170673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:12.343755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:13.520521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:34:21.031611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
범죄대분류범죄중분류수사_형사외근_112차교통검문소기타 경찰방범원경비원피해자기타 민간인기타미상
범죄대분류1.0001.0000.8820.9491.0000.9660.8220.8681.0000.8760.9740.8770.845
범죄중분류1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
수사_형사0.8821.0001.0000.6910.0000.7200.7670.7460.7550.8160.6670.9360.893
외근_112차0.9491.0000.6911.0001.0000.7190.9320.9500.9951.0000.8190.7380.721
교통1.0001.0000.0001.0001.0001.0001.0000.5881.0001.0000.5390.8340.539
검문소0.9661.0000.7200.7191.0001.0000.9920.4890.7900.9960.6780.9590.817
기타 경찰0.8221.0000.7670.9321.0000.9921.0000.7630.9320.9820.7970.9430.881
방범원0.8681.0000.7460.9500.5880.4890.7631.0000.9310.9020.8070.6600.590
경비원1.0001.0000.7550.9951.0000.7900.9320.9311.0001.0000.8980.6540.721
피해자0.8761.0000.8161.0001.0000.9960.9820.9021.0001.0000.8440.9420.898
기타 민간인0.9741.0000.6670.8190.5390.6780.7970.8070.8980.8441.0000.3570.842
기타0.8771.0000.9360.7380.8340.9590.9430.6600.6540.9420.3571.0000.924
미상0.8451.0000.8930.7210.5390.8170.8810.5900.7210.8980.8420.9241.000
2023-12-12T19:34:21.314145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방범원범죄대분류검문소
방범원1.0000.5740.478
범죄대분류0.5741.0000.639
검문소0.4780.6391.000
2023-12-12T19:34:21.485679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수사_형사외근_112차교통기타 경찰경비원피해자기타 민간인기타미상범죄대분류검문소방범원
수사_형사1.0000.8440.8400.8680.7160.7660.7400.9310.8700.5620.6010.594
외근_112차0.8441.0000.8490.8420.7610.8190.7780.7700.7000.7130.7490.700
교통0.8400.8491.0000.8760.7010.8630.7550.8350.7810.7990.9860.391
기타 경찰0.8680.8420.8761.0000.7490.8180.7620.8780.8210.4680.8530.583
경비원0.7160.7610.7010.7491.0000.7930.7270.6480.6680.8220.8370.649
피해자0.7660.8190.8630.8180.7931.0000.8460.7520.7510.5430.8800.761
기타 민간인0.7400.7780.7550.7620.7270.8461.0000.7080.6510.6500.6330.757
기타0.9310.7700.8350.8780.6480.7520.7081.0000.8760.5460.7200.472
미상0.8700.7000.7810.8210.6680.7510.6510.8761.0000.4330.8190.507
범죄대분류0.5620.7130.7990.4680.8220.5430.6500.5460.4331.0000.6390.574
검문소0.6010.7490.9860.8530.8370.8800.6330.7200.8190.6391.0000.478
방범원0.5940.7000.3910.5830.6490.7610.7570.4720.5070.5740.4781.000

Missing values

2023-12-12T19:34:14.811032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:34:15.064790image/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

범죄대분류범죄중분류수사_형사외근_112차교통검문소기타 경찰방범원경비원피해자기타 민간인기타미상
0강력범죄살인기수168710060011160
1강력범죄살인미수등14422920120021238
2강력범죄강도57112010200158062
3강력범죄강간15495521023300193251255
4강력범죄유사강간29610420396000011114
5강력범죄강제추행505325286057621169371372206
6강력범죄기타 강간 강제추행등10828401070022132
7강력범죄방화5964760070002279
8절도범죄절도범죄74465994578031377619223352937216
9폭력범죄상해20765715810703301028691442096
범죄대분류범죄중분류수사_형사외근_112차교통검문소기타 경찰방범원경비원피해자기타 민간인기타미상
28특별경제범죄특별경제범죄30652812420013791039653418316
29마약범죄마약범죄1052335740536000256910
30보건범죄보건범죄4395838648029902151911146931
31환경범죄환경범죄400852102170021175658
32교통범죄교통범죄11038877961113326125691747762334319278
33노동범죄노동범죄6074002800001396
34안보범죄안보범죄5511006500016174
35선거범죄선거범죄27356700720001231395
36병역범죄병역범죄345822401930021145951
37기타범죄기타범죄129770271073515126939218220109311268188