Overview

Dataset statistics

Number of variables11
Number of observations38
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory100.5 B

Variable types

Categorical1
Text1
Numeric9

Dataset

Description강력범죄, 절도범죄, 폭력범죄, 지능범죄, 풍속범죄, 특별경제 범죄, 마약범죄, 보건범죄, 환경범죄, 교통범죄, 노동범죄, 안보범죄, 선거범죄, 병역범죄, 기타범죄를 범죄발생부터 검거까지의 기간 제공
Author경찰청
URLhttps://www.data.go.kr/data/3074461/fileData.do

Alerts

1일이내 is highly overall correlated with 2일이내 and 8 other fieldsHigh correlation
2일이내 is highly overall correlated with 1일이내 and 8 other fieldsHigh correlation
3일이내 is highly overall correlated with 1일이내 and 8 other fieldsHigh correlation
10일이내 is highly overall correlated with 1일이내 and 7 other fieldsHigh correlation
1개월이내 is highly overall correlated with 1일이내 and 7 other fieldsHigh correlation
3개월이내 is highly overall correlated with 1일이내 and 8 other fieldsHigh correlation
6개월이내 is highly overall correlated with 1일이내 and 8 other fieldsHigh correlation
1년이내 is highly overall correlated with 1일이내 and 8 other fieldsHigh correlation
1년초과 is highly overall correlated with 1일이내 and 7 other fieldsHigh correlation
범죄대분류 is highly overall correlated with 1일이내 and 5 other fieldsHigh correlation
범죄중분류 has unique valuesUnique
3개월이내 has unique valuesUnique
6개월이내 has unique valuesUnique
2일이내 has 4 (10.5%) zerosZeros
3일이내 has 2 (5.3%) zerosZeros
1년초과 has 1 (2.6%) zerosZeros

Reproduction

Analysis started2023-12-12 08:50:51.406714
Analysis finished2023-12-12 08:51:01.510132
Duration10.1 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

2023-12-12T17:51:01.601147image/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
2023-12-12T17:51:01.842961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length6
Mean length3.7894737
Min length2

Characters and Unicode

Total characters144
Distinct characters74
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

Unique38 ?
Unique (%)100.0%

Sample

1st row살인기수
2nd row살인미수등
3rd row강도
4th row강간
5th row유사강간
ValueCountFrequency (%)
강간 2
 
5.0%
살인기수 1
 
2.5%
도박범죄 1
 
2.5%
병역범죄 1
 
2.5%
문서-인장 1
 
2.5%
유가증권인지 1
 
2.5%
사기 1
 
2.5%
횡령 1
 
2.5%
배임 1
 
2.5%
성풍속범죄 1
 
2.5%
Other values (29) 29
72.5%
2023-12-12T17:51:02.306016image/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%
인 5
 
3.5%
유 4
 
2.8%
행 4
 
2.8%
제 3
 
2.1%
간 3
 
2.1%
등 3
 
2.1%
Other values (64) 85
59.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 139
96.5%
Dash Punctuation 3
 
2.1%
Space Separator 2
 
1.4%

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%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Space Separator
ValueCountFrequency (%)
2
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 (%)
- 3
60.0%
2
40.0%

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 (%)
- 3
60.0%
2
40.0%

1일이내
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6569.2895
Minimum2
Maximum126526
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T17:51:02.512724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.85
Q161.25
median311
Q32619.5
95-th percentile29280.2
Maximum126526
Range126524
Interquartile range (IQR)2558.25

Descriptive statistics

Standard deviation21190.994
Coefficient of variation (CV)3.2257666
Kurtosis29.48482
Mean6569.2895
Median Absolute Deviation (MAD)308.5
Skewness5.2330704
Sum249633
Variance4.4905825 × 108
MonotonicityNot monotonic
2023-12-12T17:51:02.703732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
15 2
 
5.3%
6 2
 
5.3%
8352 1
 
2.6%
276 1
 
2.6%
3 1
 
2.6%
7891 1
 
2.6%
914 1
 
2.6%
2354 1
 
2.6%
950 1
 
2.6%
1850 1
 
2.6%
Other values (26) 26
68.4%
ValueCountFrequency (%)
2 1
2.6%
3 1
2.6%
4 1
2.6%
6 2
5.3%
11 1
2.6%
15 2
5.3%
44 1
2.6%
56 1
2.6%
77 1
2.6%
105 1
2.6%
ValueCountFrequency (%)
126526 1
2.6%
31021 1
2.6%
28973 1
2.6%
12881 1
2.6%
8352 1
2.6%
7891 1
2.6%
7242 1
2.6%
6145 1
2.6%
5775 1
2.6%
2708 1
2.6%

2일이내
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean341.18421
Minimum0
Maximum5001
Zeros4
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T17:51:02.893304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.5
median32
Q3149
95-th percentile1730.35
Maximum5001
Range5001
Interquartile range (IQR)145.5

Descriptive statistics

Standard deviation914.52533
Coefficient of variation (CV)2.6804445
Kurtosis18.863631
Mean341.18421
Median Absolute Deviation (MAD)31.5
Skewness4.0999588
Sum12965
Variance836356.59
MonotonicityNot monotonic
2023-12-12T17:51:03.048343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 4
 
10.5%
5 3
 
7.9%
2 3
 
7.9%
1 2
 
5.3%
36 2
 
5.3%
94 1
 
2.6%
3 1
 
2.6%
1550 1
 
2.6%
74 1
 
2.6%
47 1
 
2.6%
Other values (19) 19
50.0%
ValueCountFrequency (%)
0 4
10.5%
1 2
5.3%
2 3
7.9%
3 1
 
2.6%
5 3
7.9%
7 1
 
2.6%
14 1
 
2.6%
21 1
 
2.6%
22 1
 
2.6%
24 1
 
2.6%
ValueCountFrequency (%)
5001 1
2.6%
2112 1
2.6%
1663 1
2.6%
1550 1
2.6%
550 1
2.6%
391 1
2.6%
325 1
2.6%
227 1
2.6%
162 1
2.6%
152 1
2.6%

3일이내
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean460.34211
Minimum0
Maximum6557
Zeros2
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T17:51:03.199582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.85
Q13.25
median40.5
Q3212.25
95-th percentile2363.7
Maximum6557
Range6557
Interquartile range (IQR)209

Descriptive statistics

Standard deviation1226.9406
Coefficient of variation (CV)2.6652801
Kurtosis17.249412
Mean460.34211
Median Absolute Deviation (MAD)39.5
Skewness3.9492981
Sum17493
Variance1505383.3
MonotonicityNot monotonic
2023-12-12T17:51:03.362524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 5
 
13.2%
8 3
 
7.9%
0 2
 
5.3%
3 2
 
5.3%
42 2
 
5.3%
12 1
 
2.6%
560 1
 
2.6%
1884 1
 
2.6%
74 1
 
2.6%
2 1
 
2.6%
Other values (19) 19
50.0%
ValueCountFrequency (%)
0 2
 
5.3%
1 5
13.2%
2 1
 
2.6%
3 2
 
5.3%
4 1
 
2.6%
8 3
7.9%
12 1
 
2.6%
14 1
 
2.6%
21 1
 
2.6%
29 1
 
2.6%
ValueCountFrequency (%)
6557 1
2.6%
3314 1
2.6%
2196 1
2.6%
1884 1
2.6%
666 1
2.6%
560 1
2.6%
531 1
2.6%
426 1
2.6%
229 1
2.6%
227 1
2.6%

10일이내
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3101.5
Minimum2
Maximum32045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T17:51:03.504678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.55
Q120.25
median292
Q31870.75
95-th percentile18963.55
Maximum32045
Range32043
Interquartile range (IQR)1850.5

Descriptive statistics

Standard deviation7092.8587
Coefficient of variation (CV)2.2869124
Kurtosis8.3931818
Mean3101.5
Median Absolute Deviation (MAD)286.5
Skewness2.9359348
Sum117857
Variance50308644
MonotonicityNot monotonic
2023-12-12T17:51:03.653950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
6 3
 
7.9%
2 2
 
5.3%
16670 1
 
2.6%
651 1
 
2.6%
201 1
 
2.6%
21 1
 
2.6%
32045 1
 
2.6%
354 1
 
2.6%
1870 1
 
2.6%
15 1
 
2.6%
Other values (25) 25
65.8%
ValueCountFrequency (%)
2 2
5.3%
5 1
 
2.6%
6 3
7.9%
11 1
 
2.6%
15 1
 
2.6%
19 1
 
2.6%
20 1
 
2.6%
21 1
 
2.6%
22 1
 
2.6%
35 1
 
2.6%
ValueCountFrequency (%)
32045 1
2.6%
22293 1
2.6%
18376 1
2.6%
16670 1
2.6%
5153 1
2.6%
4686 1
2.6%
4118 1
2.6%
2512 1
2.6%
2232 1
2.6%
1871 1
2.6%

1개월이내
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5892.4474
Minimum4
Maximum40628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T17:51:03.828672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q143.75
median643.5
Q34679.5
95-th percentile36192.15
Maximum40628
Range40624
Interquartile range (IQR)4635.75

Descriptive statistics

Standard deviation11633.349
Coefficient of variation (CV)1.9742814
Kurtosis3.7204877
Mean5892.4474
Median Absolute Deviation (MAD)627.5
Skewness2.2441135
Sum223913
Variance1.3533482 × 108
MonotonicityNot monotonic
2023-12-12T17:51:04.020145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5 2
 
5.3%
6103 1
 
2.6%
1032 1
 
2.6%
4 1
 
2.6%
22569 1
 
2.6%
5998 1
 
2.6%
143 1
 
2.6%
2048 1
 
2.6%
393 1
 
2.6%
10 1
 
2.6%
Other values (27) 27
71.1%
ValueCountFrequency (%)
4 1
2.6%
5 2
5.3%
10 1
2.6%
12 1
2.6%
20 1
2.6%
27 1
2.6%
34 1
2.6%
35 1
2.6%
39 1
2.6%
58 1
2.6%
ValueCountFrequency (%)
40628 1
2.6%
37315 1
2.6%
35994 1
2.6%
34477 1
2.6%
22569 1
2.6%
9532 1
2.6%
7555 1
2.6%
6103 1
2.6%
5998 1
2.6%
4739 1
2.6%

3개월이내
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6601.2105
Minimum7
Maximum61598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T17:51:04.170041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile11.85
Q198
median818.5
Q35344.25
95-th percentile33525.45
Maximum61598
Range61591
Interquartile range (IQR)5246.25

Descriptive statistics

Standard deviation13864.478
Coefficient of variation (CV)2.1002933
Kurtosis9.1006643
Mean6601.2105
Median Absolute Deviation (MAD)807
Skewness3.0133507
Sum250846
Variance1.9222376 × 108
MonotonicityNot monotonic
2023-12-12T17:51:04.320588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
11 1
 
2.6%
1758 1
 
2.6%
7 1
 
2.6%
61598 1
 
2.6%
7639 1
 
2.6%
672 1
 
2.6%
6937 1
 
2.6%
539 1
 
2.6%
9451 1
 
2.6%
4832 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
7 1
2.6%
11 1
2.6%
12 1
2.6%
20 1
2.6%
36 1
2.6%
50 1
2.6%
52 1
2.6%
58 1
2.6%
73 1
2.6%
92 1
2.6%
ValueCountFrequency (%)
61598 1
2.6%
53418 1
2.6%
30015 1
2.6%
21587 1
2.6%
19443 1
2.6%
9451 1
2.6%
8286 1
2.6%
7639 1
2.6%
6937 1
2.6%
5515 1
2.6%

6개월이내
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2854.3684
Minimum2
Maximum37454
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T17:51:04.456684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10.4
Q150.25
median440.5
Q31810.5
95-th percentile10738.9
Maximum37454
Range37452
Interquartile range (IQR)1760.25

Descriptive statistics

Standard deviation7499.8571
Coefficient of variation (CV)2.6275014
Kurtosis15.721601
Mean2854.3684
Median Absolute Deviation (MAD)427.5
Skewness3.9614351
Sum108466
Variance56247856
MonotonicityNot monotonic
2023-12-12T17:51:04.604137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
14 1
 
2.6%
1485 1
 
2.6%
7 1
 
2.6%
37454 1
 
2.6%
3034 1
 
2.6%
545 1
 
2.6%
3233 1
 
2.6%
278 1
 
2.6%
5434 1
 
2.6%
1919 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
2 1
2.6%
7 1
2.6%
11 1
2.6%
12 1
2.6%
14 1
2.6%
19 1
2.6%
23 1
2.6%
31 1
2.6%
37 1
2.6%
47 1
2.6%
ValueCountFrequency (%)
37454 1
2.6%
28815 1
2.6%
7549 1
2.6%
5434 1
2.6%
4284 1
2.6%
3876 1
2.6%
3233 1
2.6%
3034 1
2.6%
2089 1
2.6%
1919 1
2.6%

1년이내
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1847.5263
Minimum2
Maximum26005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T17:51:04.772866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.7
Q132
median301
Q31106.25
95-th percentile6728.55
Maximum26005
Range26003
Interquartile range (IQR)1074.25

Descriptive statistics

Standard deviation5467.7023
Coefficient of variation (CV)2.959472
Kurtosis15.750063
Mean1847.5263
Median Absolute Deviation (MAD)287
Skewness4.0502268
Sum70206
Variance29895769
MonotonicityNot monotonic
2023-12-12T17:51:04.904912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
14 2
 
5.3%
9 1
 
2.6%
1586 1
 
2.6%
3 1
 
2.6%
26005 1
 
2.6%
1840 1
 
2.6%
394 1
 
2.6%
1694 1
 
2.6%
462 1
 
2.6%
3888 1
 
2.6%
Other values (27) 27
71.1%
ValueCountFrequency (%)
2 1
2.6%
3 1
2.6%
5 1
2.6%
7 1
2.6%
9 1
2.6%
11 1
2.6%
14 2
5.3%
15 1
2.6%
30 1
2.6%
38 1
2.6%
ValueCountFrequency (%)
26005 1
2.6%
22825 1
2.6%
3888 1
2.6%
2597 1
2.6%
1840 1
2.6%
1694 1
2.6%
1586 1
2.6%
1419 1
2.6%
1264 1
2.6%
1151 1
2.6%

1년초과
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2168.6579
Minimum0
Maximum30587
Zeros1
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T17:51:05.044934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.7
Q147
median320
Q3989.75
95-th percentile7444.8
Maximum30587
Range30587
Interquartile range (IQR)942.75

Descriptive statistics

Standard deviation6679.4254
Coefficient of variation (CV)3.0799811
Kurtosis15.645417
Mean2168.6579
Median Absolute Deviation (MAD)300.5
Skewness4.0602086
Sum82409
Variance44614724
MonotonicityNot monotonic
2023-12-12T17:51:05.215615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
45 2
 
5.3%
12 1
 
2.6%
3612 1
 
2.6%
9 1
 
2.6%
30587 1
 
2.6%
3500 1
 
2.6%
1031 1
 
2.6%
999 1
 
2.6%
804 1
 
2.6%
1426 1
 
2.6%
Other values (27) 27
71.1%
ValueCountFrequency (%)
0 1
2.6%
9 1
2.6%
11 1
2.6%
12 1
2.6%
14 1
2.6%
15 1
2.6%
24 1
2.6%
37 1
2.6%
45 2
5.3%
53 1
2.6%
ValueCountFrequency (%)
30587 1
2.6%
29164 1
2.6%
3612 1
2.6%
3500 1
2.6%
2195 1
2.6%
1985 1
2.6%
1426 1
2.6%
1231 1
2.6%
1031 1
2.6%
999 1
2.6%

Interactions

2023-12-12T17:51:00.023634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:51.850229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:52.898838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:53.872746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:55.001105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:55.981150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:57.004177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:58.030595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:59.127673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:00.131062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:51.951486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:52.999156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:53.969152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:55.102450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:56.104828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:57.093409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:58.159059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:59.222968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:00.229276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:52.078493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:53.088495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:54.358316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:55.193873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:56.222123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:57.192201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:58.270778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:59.324569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:00.332566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:52.200000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:53.172653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:54.438141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:55.292112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:56.333481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:57.296378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:58.383873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:59.431083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:00.435635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:52.299822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:53.280958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:54.525651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:55.382604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:56.436043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:57.397151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:58.504998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:59.525332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:00.533486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:52.417980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:53.421553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:54.618557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:55.516455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:56.551764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:57.525725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:58.670492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:59.631986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:00.621611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:52.541353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:53.535945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:54.727825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:55.638956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:56.672936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:57.655711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:58.785961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:59.747132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:00.708805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:52.666600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:53.645333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:54.828362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:55.757896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:56.801339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:57.781400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:58.909244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:59.844764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:00.789871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:52.797599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:53.759866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:54.918247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:55.875749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:56.911711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:57.912326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:59.023158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:59.936655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:51:05.317704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
범죄대분류범죄중분류1일이내2일이내3일이내10일이내1개월이내3개월이내6개월이내1년이내1년초과
범죄대분류1.0001.0000.9490.9330.8800.8600.6010.8480.9510.8760.781
범죄중분류1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
1일이내0.9491.0001.0001.0001.0000.8640.8020.9440.8210.6480.206
2일이내0.9331.0001.0001.0000.9010.9750.8460.9240.9890.7210.593
3일이내0.8801.0001.0000.9011.0000.9290.9680.9760.8640.6560.681
10일이내0.8601.0000.8640.9750.9291.0000.8050.8290.9400.4980.387
1개월이내0.6011.0000.8020.8460.9680.8051.0000.9630.8290.8380.936
3개월이내0.8481.0000.9440.9240.9760.8290.9631.0000.9630.9410.982
6개월이내0.9511.0000.8210.9890.8640.9400.8290.9631.0000.8740.726
1년이내0.8761.0000.6480.7210.6560.4980.8380.9410.8741.0000.789
1년초과0.7811.0000.2060.5930.6810.3870.9360.9820.7260.7891.000
2023-12-12T17:51:05.471669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1일이내2일이내3일이내10일이내1개월이내3개월이내6개월이내1년이내1년초과범죄대분류
1일이내1.0000.9330.9430.9200.8990.8580.8330.7810.6760.713
2일이내0.9331.0000.9790.9760.9590.9180.8840.8160.7280.557
3일이내0.9430.9791.0000.9650.9570.9200.8870.8190.7240.551
10일이내0.9200.9760.9651.0000.9700.9290.8850.8300.7460.451
1개월이내0.8990.9590.9570.9701.0000.9820.9550.8970.8190.262
3개월이내0.8580.9180.9200.9290.9821.0000.9840.9410.8720.503
6개월이내0.8330.8840.8870.8850.9550.9841.0000.9710.9060.592
1년이내0.7810.8160.8190.8300.8970.9410.9711.0000.9570.587
1년초과0.6760.7280.7240.7460.8190.8720.9060.9571.0000.404
범죄대분류0.7130.5570.5510.4510.2620.5030.5920.5870.4041.000

Missing values

2023-12-12T17:51:01.217023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:51:01.443475image/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

범죄대분류범죄중분류1일이내2일이내3일이내10일이내1개월이내3개월이내6개월이내1년이내1년초과
0강력범죄살인기수1855811101114912
1강력범죄살인미수등3195415202011715
2강력범죄강도2492112515858231124
3강력범죄강간66836432308551709708387515
4강력범죄유사강간11328351803251094577
5강력범죄강제추행2708941091111370447521176541758
6강력범죄기타 강간 강제추행등4420203552191537
7강력범죄방화632222193131140471411
8절도범죄절도범죄1288121122196183763599430015754925971985
9폭력범죄상해57753255314118755555151273452483
범죄대분류범죄중분류1일이내2일이내3일이내10일이내1개월이내3개월이내6개월이내1년이내1년초과
28특별경제범죄특별경제범죄8352140227187161039451543438883612
29마약범죄마약범죄185013510145810821758148515861426
30보건범죄보건범죄161215216818704501483219191151602
31환경범죄환경범죄1252829354892928386151193
32교통범죄교통범죄12652650016557320453731519443387614191231
33노동범죄노동범죄6222171151137183113
34안보범죄안보범죄6116550121474
35선거범죄선거범죄193474220143269241511245
36병역범죄병역범죄77747465110501122297137368
37기타범죄기타범죄3102115501884166704062853418288152282529164