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

Categorical1
Text1
Numeric11

Dataset

Description전국 경찰관서에 고소, 고발, 인지 등으로 형사입건된 사건의 발생, 검거, 피의자에 대한 죄종별 분석 현황 1시간,2시간,5시간,12시간,24시간,2일,5일,10일,1개월,3개월 등으로 구분
Author경찰청
URLhttps://www.data.go.kr/data/3074460/fileData.do

Alerts

1시간이내 is highly overall correlated with 2시간이내 and 10 other fieldsHigh correlation
2시간이내 is highly overall correlated with 1시간이내 and 9 other fieldsHigh correlation
5시간이내 is highly overall correlated with 1시간이내 and 9 other fieldsHigh correlation
12시간이내 is highly overall correlated with 1시간이내 and 9 other fieldsHigh correlation
24시간이내 is highly overall correlated with 1시간이내 and 9 other fieldsHigh correlation
2일이내 is highly overall correlated with 1시간이내 and 9 other fieldsHigh correlation
5일이내 is highly overall correlated with 1시간이내 and 9 other fieldsHigh correlation
10일이내 is highly overall correlated with 1시간이내 and 9 other fieldsHigh correlation
1개월이내 is highly overall correlated with 1시간이내 and 9 other fieldsHigh correlation
3개월이내 is highly overall correlated with 1시간이내 and 10 other fieldsHigh correlation
3개월초과 is highly overall correlated with 1시간이내 and 9 other fieldsHigh correlation
범죄대분류 is highly overall correlated with 1시간이내 and 1 other fieldsHigh correlation
1개월이내 has unique valuesUnique
3개월이내 has unique valuesUnique
3개월초과 has unique valuesUnique
2시간이내 has 4 (10.5%) zerosZeros
5시간이내 has 2 (5.3%) zerosZeros

Reproduction

Analysis started2023-12-12 09:59:56.605557
Analysis finished2023-12-12 10:00:10.539880
Duration13.93 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

2023-12-12T19:00:10.913454image/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

Distinct28
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-12T19:00:11.096259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length2
Mean length3.1578947
Min length2

Characters and Unicode

Total characters120
Distinct characters60
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)71.1%

Sample

1st row살인기수
2nd row살인미수등
3rd row강도
4th row강간
5th row유사강간
ValueCountFrequency (%)
소계 11
28.2%
폭력행위등 1
 
2.6%
공갈 1
 
2.6%
성풍속범죄 1
 
2.6%
배임 1
 
2.6%
횡령 1
 
2.6%
사기 1
 
2.6%
유가증권인지 1
 
2.6%
문서,인장 1
 
2.6%
통화 1
 
2.6%
Other values (19) 19
48.7%
2023-12-12T19:00:11.396694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
소 11
 
9.2%
계 11
 
9.2%
강 6
 
5.0%
인 5
 
4.2%
행 4
 
3.3%
유 4
 
3.3%
, 4
 
3.3%
기 4
 
3.3%
등 3
 
2.5%
수 3
 
2.5%
Other values (50) 65
54.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 115
95.8%
Other Punctuation 4
 
3.3%
Space Separator 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 11
 
9.6%
계 11
 
9.6%
강 6
 
5.2%
인 5
 
4.3%
행 4
 
3.5%
유 4
 
3.5%
기 4
 
3.5%
등 3
 
2.6%
수 3
 
2.6%
간 3
 
2.6%
Other values (48) 61
53.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 115
95.8%
Common 5
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 11
 
9.6%
계 11
 
9.6%
강 6
 
5.2%
인 5
 
4.3%
행 4
 
3.5%
유 4
 
3.5%
기 4
 
3.5%
등 3
 
2.6%
수 3
 
2.6%
간 3
 
2.6%
Other values (48) 61
53.0%
Common
ValueCountFrequency (%)
, 4
80.0%
1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 115
95.8%
ASCII 5
 
4.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
소 11
 
9.6%
계 11
 
9.6%
강 6
 
5.2%
인 5
 
4.3%
행 4
 
3.5%
유 4
 
3.5%
기 4
 
3.5%
등 3
 
2.6%
수 3
 
2.6%
간 3
 
2.6%
Other values (48) 61
53.0%
ASCII
ValueCountFrequency (%)
, 4
80.0%
1
 
20.0%

1시간이내
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5635.4474
Minimum6
Maximum107822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:00:11.534070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile14.25
Q164.5
median346
Q32930.5
95-th percentile19740.05
Maximum107822
Range107816
Interquartile range (IQR)2866

Descriptive statistics

Standard deviation18001.156
Coefficient of variation (CV)3.1942727
Kurtosis29.820877
Mean5635.4474
Median Absolute Deviation (MAD)324
Skewness5.2573037
Sum214147
Variance3.2404162 × 108
MonotonicityNot monotonic
2023-12-12T19:00:11.662987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
22 2
 
5.3%
60 1
 
2.6%
4476 1
 
2.6%
6 1
 
2.6%
17460 1
 
2.6%
4241 1
 
2.6%
229 1
 
2.6%
1788 1
 
2.6%
460 1
 
2.6%
633 1
 
2.6%
Other values (27) 27
71.1%
ValueCountFrequency (%)
6 1
2.6%
10 1
2.6%
15 1
2.6%
22 2
5.3%
23 1
2.6%
24 1
2.6%
30 1
2.6%
50 1
2.6%
60 1
2.6%
78 1
2.6%
ValueCountFrequency (%)
107822 1
2.6%
25577 1
2.6%
18710 1
2.6%
17460 1
2.6%
12325 1
2.6%
5653 1
2.6%
4476 1
2.6%
4241 1
2.6%
4165 1
2.6%
3232 1
2.6%

2시간이내
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean659.07895
Minimum0
Maximum9362
Zeros4
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:00:11.810031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.25
median29
Q3495.5
95-th percentile2958.95
Maximum9362
Range9362
Interquartile range (IQR)493.25

Descriptive statistics

Standard deviation1692.4367
Coefficient of variation (CV)2.5678816
Kurtosis19.521837
Mean659.07895
Median Absolute Deviation (MAD)29
Skewness4.1346967
Sum25045
Variance2864342.1
MonotonicityNot monotonic
2023-12-12T19:00:11.942034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 4
 
10.5%
1 4
 
10.5%
2 2
 
5.3%
29 2
 
5.3%
64 1
 
2.6%
18 1
 
2.6%
1520 1
 
2.6%
679 1
 
2.6%
287 1
 
2.6%
8 1
 
2.6%
Other values (20) 20
52.6%
ValueCountFrequency (%)
0 4
10.5%
1 4
10.5%
2 2
5.3%
3 1
 
2.6%
4 1
 
2.6%
6 1
 
2.6%
8 1
 
2.6%
9 1
 
2.6%
15 1
 
2.6%
16 1
 
2.6%
ValueCountFrequency (%)
9362 1
2.6%
3837 1
2.6%
2804 1
2.6%
2625 1
2.6%
1520 1
2.6%
1015 1
2.6%
738 1
2.6%
697 1
2.6%
679 1
2.6%
556 1
2.6%

5시간이내
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean885.63158
Minimum0
Maximum7555
Zeros2
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:00:12.113806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.85
Q15.5
median57.5
Q3925.25
95-th percentile5115.25
Maximum7555
Range7555
Interquartile range (IQR)919.75

Descriptive statistics

Standard deviation1791.7991
Coefficient of variation (CV)2.0231879
Kurtosis5.7078349
Mean885.63158
Median Absolute Deviation (MAD)56.5
Skewness2.4873581
Sum33654
Variance3210544
MonotonicityNot monotonic
2023-12-12T19:00:12.246451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 3
 
7.9%
3 2
 
5.3%
153 2
 
5.3%
0 2
 
5.3%
5 2
 
5.3%
481 1
 
2.6%
9 1
 
2.6%
25 1
 
2.6%
2791 1
 
2.6%
1096 1
 
2.6%
Other values (22) 22
57.9%
ValueCountFrequency (%)
0 2
5.3%
1 3
7.9%
2 1
 
2.6%
3 2
5.3%
5 2
5.3%
7 1
 
2.6%
8 1
 
2.6%
9 1
 
2.6%
14 1
 
2.6%
25 1
 
2.6%
ValueCountFrequency (%)
7555 1
2.6%
5485 1
2.6%
5050 1
2.6%
4407 1
2.6%
2791 1
2.6%
1719 1
2.6%
1290 1
2.6%
1096 1
2.6%
1066 1
2.6%
1055 1
2.6%

12시간이내
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1342.2632
Minimum4
Maximum9455
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:00:12.381925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile7.7
Q153.25
median176.5
Q31170.5
95-th percentile8126.55
Maximum9455
Range9451
Interquartile range (IQR)1117.25

Descriptive statistics

Standard deviation2544.3792
Coefficient of variation (CV)1.895589
Kurtosis4.0869204
Mean1342.2632
Median Absolute Deviation (MAD)158
Skewness2.2732163
Sum51006
Variance6473865.6
MonotonicityNot monotonic
2023-12-12T19:00:12.523734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
69 2
 
5.3%
17 2
 
5.3%
48 1
 
2.6%
298 1
 
2.6%
8294 1
 
2.6%
1892 1
 
2.6%
142 1
 
2.6%
610 1
 
2.6%
238 1
 
2.6%
2014 1
 
2.6%
Other values (26) 26
68.4%
ValueCountFrequency (%)
4 1
2.6%
6 1
2.6%
8 1
2.6%
17 2
5.3%
20 1
2.6%
24 1
2.6%
25 1
2.6%
45 1
2.6%
48 1
2.6%
69 2
5.3%
ValueCountFrequency (%)
9455 1
2.6%
8294 1
2.6%
8097 1
2.6%
6570 1
2.6%
4707 1
2.6%
2402 1
2.6%
2014 1
2.6%
1892 1
2.6%
1309 1
2.6%
1239 1
2.6%

24시간이내
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1426.9474
Minimum1
Maximum9618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:00:12.673208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.1
Q146.25
median152.5
Q31040.25
95-th percentile8925.6
Maximum9618
Range9617
Interquartile range (IQR)994

Descriptive statistics

Standard deviation2790.0274
Coefficient of variation (CV)1.955242
Kurtosis3.9126764
Mean1426.9474
Median Absolute Deviation (MAD)141.5
Skewness2.2720146
Sum54224
Variance7784253
MonotonicityNot monotonic
2023-12-12T19:00:12.853038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
86 2
 
5.3%
13 2
 
5.3%
36 1
 
2.6%
1720 1
 
2.6%
1 1
 
2.6%
9618 1
 
2.6%
2374 1
 
2.6%
85 1
 
2.6%
756 1
 
2.6%
199 1
 
2.6%
Other values (26) 26
68.4%
ValueCountFrequency (%)
1 1
2.6%
3 1
2.6%
9 1
2.6%
13 2
5.3%
15 1
2.6%
16 1
2.6%
35 1
2.6%
36 1
2.6%
46 1
2.6%
47 1
2.6%
ValueCountFrequency (%)
9618 1
2.6%
9133 1
2.6%
8889 1
2.6%
8426 1
2.6%
4157 1
2.6%
2966 1
2.6%
2374 1
2.6%
1720 1
2.6%
1240 1
2.6%
1135 1
2.6%

2일이내
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1475.4737
Minimum1
Maximum14049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:00:13.005834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.85
Q127.75
median118
Q31034.5
95-th percentile7793.15
Maximum14049
Range14048
Interquartile range (IQR)1006.75

Descriptive statistics

Standard deviation3114.6223
Coefficient of variation (CV)2.1109304
Kurtosis7.4237467
Mean1475.4737
Median Absolute Deviation (MAD)109
Skewness2.7210907
Sum56068
Variance9700871.9
MonotonicityNot monotonic
2023-12-12T19:00:13.148394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
31 2
 
5.3%
381 1
 
2.6%
174 1
 
2.6%
2 1
 
2.6%
7517 1
 
2.6%
2006 1
 
2.6%
814 1
 
2.6%
143 1
 
2.6%
1378 1
 
2.6%
14 1
 
2.6%
Other values (27) 27
71.1%
ValueCountFrequency (%)
1 1
2.6%
2 1
2.6%
3 1
2.6%
8 1
2.6%
10 1
2.6%
12 1
2.6%
13 1
2.6%
14 1
2.6%
19 1
2.6%
27 1
2.6%
ValueCountFrequency (%)
14049 1
2.6%
9358 1
2.6%
7517 1
2.6%
7192 1
2.6%
5529 1
2.6%
2646 1
2.6%
2006 1
2.6%
1435 1
2.6%
1378 1
2.6%
1108 1
2.6%

5일이내
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3275.4474
Minimum1
Maximum34715
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:00:13.301790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.25
Q131.75
median258.5
Q32071
95-th percentile16592.5
Maximum34715
Range34714
Interquartile range (IQR)2039.25

Descriptive statistics

Standard deviation7195.0105
Coefficient of variation (CV)2.1966497
Kurtosis9.7862787
Mean3275.4474
Median Absolute Deviation (MAD)240.5
Skewness2.9947847
Sum124467
Variance51768176
MonotonicityNot monotonic
2023-12-12T19:00:13.442009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
19 2
 
5.3%
21 1
 
2.6%
484 1
 
2.6%
1 1
 
2.6%
13418 1
 
2.6%
3850 1
 
2.6%
47 1
 
2.6%
1510 1
 
2.6%
295 1
 
2.6%
2666 1
 
2.6%
Other values (27) 27
71.1%
ValueCountFrequency (%)
1 1
2.6%
5 1
2.6%
10 1
2.6%
12 1
2.6%
17 1
2.6%
19 2
5.3%
21 1
2.6%
25 1
2.6%
30 1
2.6%
37 1
2.6%
ValueCountFrequency (%)
34715 1
2.6%
19032 1
2.6%
16162 1
2.6%
15231 1
2.6%
13418 1
2.6%
6028 1
2.6%
3850 1
2.6%
3435 1
2.6%
2666 1
2.6%
2252 1
2.6%

10일이내
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3780.6053
Minimum2
Maximum35627
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:00:13.588415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.85
Q136.75
median303
Q32240.5
95-th percentile19785
Maximum35627
Range35625
Interquartile range (IQR)2203.75

Descriptive statistics

Standard deviation7998.8855
Coefficient of variation (CV)2.1157685
Kurtosis7.0387124
Mean3780.6053
Median Absolute Deviation (MAD)288.5
Skewness2.6670316
Sum143663
Variance63982170
MonotonicityNot monotonic
2023-12-12T19:00:13.723382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
6 2
 
5.3%
475 1
 
2.6%
510 1
 
2.6%
17087 1
 
2.6%
5441 1
 
2.6%
104 1
 
2.6%
1528 1
 
2.6%
302 1
 
2.6%
3144 1
 
2.6%
2014 1
 
2.6%
Other values (27) 27
71.1%
ValueCountFrequency (%)
2 1
2.6%
5 1
2.6%
6 2
5.3%
8 1
2.6%
21 1
2.6%
22 1
2.6%
29 1
2.6%
34 1
2.6%
36 1
2.6%
39 1
2.6%
ValueCountFrequency (%)
35627 1
2.6%
24460 1
2.6%
18960 1
2.6%
17087 1
2.6%
16446 1
2.6%
7143 1
2.6%
5441 1
2.6%
3799 1
2.6%
3144 1
2.6%
2316 1
2.6%

1개월이내
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6892.3158
Minimum13
Maximum53367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:00:13.866440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile14.85
Q161.25
median661.5
Q34207.25
95-th percentile40777.5
Maximum53367
Range53354
Interquartile range (IQR)4146

Descriptive statistics

Standard deviation14014.585
Coefficient of variation (CV)2.0333638
Kurtosis4.3456731
Mean6892.3158
Median Absolute Deviation (MAD)640
Skewness2.3326931
Sum261908
Variance1.964086 × 108
MonotonicityNot monotonic
2023-12-12T19:00:14.015419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
18 1
 
2.6%
1216 1
 
2.6%
13 1
 
2.6%
44730 1
 
2.6%
11215 1
 
2.6%
244 1
 
2.6%
2986 1
 
2.6%
539 1
 
2.6%
6229 1
 
2.6%
4262 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
13 1
2.6%
14 1
2.6%
15 1
2.6%
18 1
2.6%
19 1
2.6%
24 1
2.6%
37 1
2.6%
49 1
2.6%
56 1
2.6%
57 1
2.6%
ValueCountFrequency (%)
53367 1
2.6%
44730 1
2.6%
40080 1
2.6%
39579 1
2.6%
25122 1
2.6%
12490 1
2.6%
11215 1
2.6%
6240 1
2.6%
6229 1
2.6%
4262 1
2.6%

3개월이내
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5761.7105
Minimum7
Maximum64870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:00:14.151304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16.55
Q175.5
median663
Q32856.5
95-th percentile30073.6
Maximum64870
Range64863
Interquartile range (IQR)2781

Descriptive statistics

Standard deviation13132.973
Coefficient of variation (CV)2.2793531
Kurtosis11.704242
Mean5761.7105
Median Absolute Deviation (MAD)635.5
Skewness3.2896153
Sum218945
Variance1.7247498 × 108
MonotonicityNot monotonic
2023-12-12T19:00:14.302044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
17 1
 
2.6%
1683 1
 
2.6%
7 1
 
2.6%
64870 1
 
2.6%
9322 1
 
2.6%
276 1
 
2.6%
2896 1
 
2.6%
896 1
 
2.6%
6555 1
 
2.6%
2738 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
7 1
2.6%
14 1
2.6%
17 1
2.6%
23 1
2.6%
26 1
2.6%
29 1
2.6%
35 1
2.6%
38 1
2.6%
40 1
2.6%
69 1
2.6%
ValueCountFrequency (%)
64870 1
2.6%
40141 1
2.6%
28297 1
2.6%
24212 1
2.6%
12951 1
2.6%
9518 1
2.6%
9322 1
2.6%
6555 1
2.6%
3521 1
2.6%
2896 1
2.6%

3개월초과
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6492.0789
Minimum20
Maximum106770
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T19:00:14.478636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile27.25
Q1120.75
median968
Q32978.5
95-th percentile19541.85
Maximum106770
Range106750
Interquartile range (IQR)2857.75

Descriptive statistics

Standard deviation20247.3
Coefficient of variation (CV)3.1187699
Kurtosis19.059826
Mean6492.0789
Median Absolute Deviation (MAD)928.5
Skewness4.3499833
Sum246699
Variance4.0995318 × 108
MonotonicityNot monotonic
2023-12-12T19:00:14.614830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
28 1
 
2.6%
2305 1
 
2.6%
23 1
 
2.6%
106770 1
 
2.6%
8305 1
 
2.6%
2304 1
 
2.6%
3313 1
 
2.6%
3102 1
 
2.6%
10539 1
 
2.6%
2176 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
20 1
2.6%
23 1
2.6%
28 1
2.6%
29 1
2.6%
38 1
2.6%
41 1
2.6%
47 1
2.6%
49 1
2.6%
65 1
2.6%
120 1
2.6%
ValueCountFrequency (%)
106770 1
2.6%
70558 1
2.6%
10539 1
2.6%
9300 1
2.6%
8305 1
2.6%
5527 1
2.6%
4939 1
2.6%
3767 1
2.6%
3313 1
2.6%
3102 1
2.6%

Interactions

2023-12-12T19:00:09.101646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:57.448046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:58.601309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:59.680750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:00.914308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:02.060165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:03.111227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:04.527132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:05.672600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:06.692728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:07.836531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:09.206779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:57.553325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:58.684592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:59.781240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:01.013132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:02.155797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:03.199171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:04.634936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:05.776306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:06.783147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:07.961531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:09.289744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:57.649626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:58.772637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:59.880581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:01.102003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:02.244384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:03.282816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:04.731500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:05.857816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:06.874336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:08.066995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:09.391288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:57.767485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:58.874984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:00.011268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:01.208330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:02.343376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:03.368049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:04.836853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:05.949803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:06.977931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:08.203052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:09.478786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:57.873278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:58.944632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:00.120015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:01.303542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:02.435964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:03.440355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:04.931022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:06.036857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:07.071503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:08.301313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:09.568942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:57.972674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:59.037768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:00.237803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:01.401050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:02.523679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:03.530170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:05.039418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:06.126746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:07.161358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:08.401024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:09.694922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:58.082268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:59.146948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:00.349957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:01.528671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:02.621319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:03.657995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:05.171974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:06.221254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:07.258081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:08.531732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:09.795964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:58.215380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:59.259269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:00.464702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:01.656207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:02.739496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:03.779990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:05.297328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:06.312008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:07.376503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:08.661990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:09.894483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:58.315842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:59.370249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:00.585944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:01.776145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:02.845208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:03.881320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:05.393187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:06.413497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:07.485843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:08.773786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:10.008528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:58.414703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:59.469671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:00.676784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:01.876417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:02.942347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:04.318010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:05.475075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:06.511038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:07.595554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:08.879128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:10.133334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:58.517384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:59.573159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:00.796601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:01.959770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:03.020267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:04.423177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:05.554380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:06.601168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:07.713886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:00:08.996848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:00:14.716638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
범죄대분류범죄중분류1시간이내2시간이내5시간이내12시간이내24시간이내2일이내5일이내10일이내1개월이내3개월이내3개월초과
범죄대분류1.0000.0000.9250.8410.7880.8410.4870.8080.7910.7980.6830.9050.795
범죄중분류0.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
1시간이내0.9250.0001.0000.8491.0000.9000.8690.9560.9561.0000.9000.9940.744
2시간이내0.8410.0000.8491.0001.0000.8940.8610.9140.8990.9190.9620.8850.677
5시간이내0.7880.0001.0001.0001.0000.9640.9570.9870.9850.9760.9610.9931.000
12시간이내0.8410.0000.9000.8940.9641.0000.9850.9180.8900.9840.9720.9240.855
24시간이내0.4870.0000.8690.8610.9570.9851.0000.8680.8590.9790.9790.8400.812
2일이내0.8080.0000.9560.9140.9870.9180.8681.0000.9920.9520.9420.9850.916
5일이내0.7910.0000.9560.8990.9850.8900.8590.9921.0000.9360.9520.9950.983
10일이내0.7980.0001.0000.9190.9760.9840.9790.9520.9361.0000.9790.9350.855
1개월이내0.6830.0000.9000.9620.9610.9720.9790.9420.9520.9791.0000.9280.855
3개월이내0.9050.0000.9940.8850.9930.9240.8400.9850.9950.9350.9281.0001.000
3개월초과0.7950.0000.7440.6771.0000.8550.8120.9160.9830.8550.8551.0001.000
2023-12-12T19:00:14.893160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1시간이내2시간이내5시간이내12시간이내24시간이내2일이내5일이내10일이내1개월이내3개월이내3개월초과범죄대분류
1시간이내1.0000.8820.8960.9930.9830.9720.9710.9750.9670.9660.8730.666
2시간이내0.8821.0000.9750.8780.9100.9210.9240.8720.8680.8660.7110.428
5시간이내0.8960.9751.0000.8920.9210.9280.9270.8780.8660.8690.7170.417
12시간이내0.9930.8780.8921.0000.9890.9700.9640.9680.9570.9600.8770.497
24시간이내0.9830.9100.9210.9891.0000.9800.9740.9670.9560.9580.8620.183
2일이내0.9720.9210.9280.9700.9801.0000.9880.9710.9640.9620.8330.451
5일이내0.9710.9240.9270.9640.9740.9881.0000.9830.9750.9710.8410.430
10일이내0.9750.8720.8780.9680.9670.9710.9831.0000.9910.9850.8720.441
1개월이내0.9670.8680.8660.9570.9560.9640.9750.9911.0000.9950.8930.324
3개월이내0.9660.8660.8690.9600.9580.9620.9710.9850.9951.0000.9140.593
3개월초과0.8730.7110.7170.8770.8620.8330.8410.8720.8930.9141.0000.417
범죄대분류0.6660.4280.4170.4970.1830.4510.4300.4410.3240.5930.4171.000

Missing values

2023-12-12T19:00:10.267188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:00:10.462251image/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

범죄대분류범죄중분류1시간이내2시간이내5시간이내12시간이내24시간이내2일이내5일이내10일이내1개월이내3개월이내3개월초과
0강력범죄살인기수60814483614216181728
1강력범죄살인미수등891639746530198141420
2강력범죄강도7815336947475434494029
3강력범죄강간366791533113202814894837848181179
4강력범죄유사강간509354546498369146124158
5강력범죄강제추행148131453656561563115281616296018611855
6강력범죄기타 강간,강제추행등24452435121921192649
7강력범죄방화17729731268660937814713047
8절도범죄소계1232528045050657091339358190322446040080282979300
9폭력범죄상해4165738105513091240143534353799624035211516
범죄대분류범죄중분류1시간이내2시간이내5시간이내12시간이내24시간이내2일이내5일이내10일이내1개월이내3개월이내3개월초과
28특별경제범죄소계44766971290201417201378266631446229655510539
29마약범죄소계63377110298426381484475121616832305
30보건범죄소계202611215596567852312872014426227382176
31환경범죄소계4752829311993258456805508639
32교통범죄소계107822936275558097888914049347153562753367242124939
33노동범죄소계30112513812225669169
34안보범죄소계1022633522429120
35선거범죄소계22611716133036573838
36병역범죄소계19400105644882141325385479
37기타범죄소계25577262544079455842671921616218960395794014170558