Overview

Dataset statistics

Number of variables11
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 KiB
Average record size in memory100.1 B

Variable types

Categorical1
Text1
Numeric9

Dataset

Description전국 경찰관서에 고소, 고발, 인지 등으로 형사입건된 사건의 발생, 검거, 피의자에 대한 죄종별 분석 현황
Author경찰청
URLhttps://www.data.go.kr/data/3074479/fileData.do

Alerts

계 is highly overall correlated with 마약류(소계) and 7 other fieldsHigh correlation
마약류(소계) is highly overall correlated with 계 and 8 other fieldsHigh correlation
마약류(마약) is highly overall correlated with 계 and 8 other fieldsHigh correlation
마약류(대마) is highly overall correlated with 계 and 7 other fieldsHigh correlation
마약류(향정신성의약품) is highly overall correlated with 계 and 7 other fieldsHigh correlation
본드신나등 is highly overall correlated with 계 and 5 other fieldsHigh correlation
알코올 is highly overall correlated with 계 and 7 other fieldsHigh correlation
해당무 is highly overall correlated with 계 and 7 other fieldsHigh correlation
미상 is highly overall correlated with 계 and 6 other fieldsHigh correlation
범죄대분류 is highly overall correlated with 마약류(소계) and 4 other fieldsHigh correlation
계 has unique valuesUnique
해당무 has unique valuesUnique
미상 has unique valuesUnique
마약류(소계) has 12 (28.6%) zerosZeros
마약류(마약) has 20 (47.6%) zerosZeros
마약류(대마) has 22 (52.4%) zerosZeros
마약류(향정신성의약품) has 13 (31.0%) zerosZeros
본드신나등 has 28 (66.7%) zerosZeros
알코올 has 9 (21.4%) zerosZeros

Reproduction

Analysis started2023-12-12 10:34:51.409126
Analysis finished2023-12-12 10:35:02.301023
Duration10.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄대분류
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Memory size468.0 B
지능범죄
10 
강력범죄
9 
폭력범죄
9 
풍속범죄
3 
절도범죄
 
1
Other values (10)
10 

Length

Max length6
Median length4
Mean length4.047619
Min length4

Unique

Unique11 ?
Unique (%)26.2%

Sample

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

Common Values

ValueCountFrequency (%)
지능범죄 10
23.8%
강력범죄 9
21.4%
폭력범죄 9
21.4%
풍속범죄 3
 
7.1%
절도범죄 1
 
2.4%
특별경제범죄 1
 
2.4%
마약범죄 1
 
2.4%
보건범죄 1
 
2.4%
환경범죄 1
 
2.4%
교통범죄 1
 
2.4%
Other values (5) 5
11.9%

Length

2023-12-12T19:35:02.410660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지능범죄 10
23.8%
강력범죄 9
21.4%
폭력범죄 9
21.4%
풍속범죄 3
 
7.1%
절도범죄 1
 
2.4%
특별경제범죄 1
 
2.4%
마약범죄 1
 
2.4%
보건범죄 1
 
2.4%
환경범죄 1
 
2.4%
교통범죄 1
 
2.4%
Other values (5) 5
11.9%

범죄중분류
Text

Distinct28
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-12T19:35:02.613795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length2
Mean length3.047619
Min length2

Characters and Unicode

Total characters128
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 (%)64.3%

Sample

1st row소계
2nd row살인기수
3rd row살인미수등
4th row강도
5th row강간
ValueCountFrequency (%)
소계 15
32.6%
강간 2
 
4.3%
폭력행위등 1
 
2.2%
성풍속범죄 1
 
2.2%
배임 1
 
2.2%
횡령 1
 
2.2%
사기 1
 
2.2%
유가증권인지 1
 
2.2%
인장 1
 
2.2%
문서 1
 
2.2%
Other values (21) 21
45.7%
2023-12-12T19:35:02.946222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
소 15
 
11.7%
계 15
 
11.7%
강 6
 
4.7%
인 5
 
3.9%
행 4
 
3.1%
유 4
 
3.1%
4
 
3.1%
기 4
 
3.1%
등 3
 
2.3%
간 3
 
2.3%
Other values (50) 65
50.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 123
96.1%
Space Separator 4
 
3.1%
Other Punctuation 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 15
 
12.2%
계 15
 
12.2%
강 6
 
4.9%
인 5
 
4.1%
행 4
 
3.3%
유 4
 
3.3%
기 4
 
3.3%
등 3
 
2.4%
간 3
 
2.4%
수 3
 
2.4%
Other values (48) 61
49.6%
Space Separator
ValueCountFrequency (%)
4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 123
96.1%
Common 5
 
3.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 15
 
12.2%
계 15
 
12.2%
강 6
 
4.9%
인 5
 
4.1%
행 4
 
3.3%
유 4
 
3.3%
기 4
 
3.3%
등 3
 
2.4%
간 3
 
2.4%
수 3
 
2.4%
Other values (48) 61
49.6%
Common
ValueCountFrequency (%)
4
80.0%
, 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 123
96.1%
ASCII 5
 
3.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
소 15
 
12.2%
계 15
 
12.2%
강 6
 
4.9%
인 5
 
4.1%
행 4
 
3.3%
유 4
 
3.3%
기 4
 
3.3%
등 3
 
2.4%
간 3
 
2.4%
수 3
 
2.4%
Other values (48) 61
49.6%
ASCII
ValueCountFrequency (%)
4
80.0%
, 1
 
20.0%

계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51851.405
Minimum86
Maximum356446
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:35:03.107353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum86
5-th percentile298.25
Q1965.5
median9977
Q335615.25
95-th percentile304571.15
Maximum356446
Range356360
Interquartile range (IQR)34649.75

Descriptive statistics

Standard deviation96657.659
Coefficient of variation (CV)1.8641281
Kurtosis3.4933359
Mean51851.405
Median Absolute Deviation (MAD)9632
Skewness2.1702651
Sum2177759
Variance9.3427031 × 109
MonotonicityNot monotonic
2023-12-12T19:35:03.256462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
26971 1
 
2.4%
67904 1
 
2.4%
15465 1
 
2.4%
117 1
 
2.4%
245009 1
 
2.4%
35930 1
 
2.4%
8324 1
 
2.4%
34671 1
 
2.4%
13546 1
 
2.4%
21125 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
86 1
2.4%
117 1
2.4%
296 1
2.4%
341 1
2.4%
349 1
2.4%
454 1
2.4%
613 1
2.4%
678 1
2.4%
807 1
2.4%
934 1
2.4%
ValueCountFrequency (%)
356446 1
2.4%
313990 1
2.4%
307706 1
2.4%
245009 1
2.4%
244539 1
2.4%
178448 1
2.4%
98425 1
2.4%
67904 1
2.4%
43552 1
2.4%
38716 1
2.4%

마약류(소계)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.64286
Minimum0
Maximum5423
Zeros12
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:35:03.395569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.5
Q313.25
95-th percentile79.55
Maximum5423
Range5423
Interquartile range (IQR)13.25

Descriptive statistics

Standard deviation834.97059
Coefficient of variation (CV)5.8535745
Kurtosis41.930541
Mean142.64286
Median Absolute Deviation (MAD)3.5
Skewness6.4729351
Sum5991
Variance697175.89
MonotonicityNot monotonic
2023-12-12T19:35:03.571733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 12
28.6%
2 4
 
9.5%
5 3
 
7.1%
3 3
 
7.1%
1 2
 
4.8%
7 2
 
4.8%
4 2
 
4.8%
80 1
 
2.4%
90 1
 
2.4%
45 1
 
2.4%
Other values (11) 11
26.2%
ValueCountFrequency (%)
0 12
28.6%
1 2
 
4.8%
2 4
 
9.5%
3 3
 
7.1%
4 2
 
4.8%
5 3
 
7.1%
6 1
 
2.4%
7 2
 
4.8%
8 1
 
2.4%
11 1
 
2.4%
ValueCountFrequency (%)
5423 1
2.4%
90 1
2.4%
80 1
2.4%
71 1
2.4%
51 1
2.4%
49 1
2.4%
45 1
2.4%
37 1
2.4%
26 1
2.4%
24 1
2.4%

마약류(마약)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.52381
Minimum0
Maximum426
Zeros20
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:35:03.696393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile17.9
Maximum426
Range426
Interquartile range (IQR)4

Descriptive statistics

Standard deviation65.461677
Coefficient of variation (CV)4.8404761
Kurtosis41.278572
Mean13.52381
Median Absolute Deviation (MAD)1
Skewness6.4007421
Sum568
Variance4285.2311
MonotonicityNot monotonic
2023-12-12T19:35:03.807707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 20
47.6%
1 4
 
9.5%
2 3
 
7.1%
3 3
 
7.1%
4 2
 
4.8%
8 2
 
4.8%
6 2
 
4.8%
15 1
 
2.4%
12 1
 
2.4%
18 1
 
2.4%
Other values (3) 3
 
7.1%
ValueCountFrequency (%)
0 20
47.6%
1 4
 
9.5%
2 3
 
7.1%
3 3
 
7.1%
4 2
 
4.8%
6 2
 
4.8%
8 2
 
4.8%
12 1
 
2.4%
15 1
 
2.4%
16 1
 
2.4%
ValueCountFrequency (%)
426 1
 
2.4%
26 1
 
2.4%
18 1
 
2.4%
16 1
 
2.4%
15 1
 
2.4%
12 1
 
2.4%
8 2
4.8%
6 2
4.8%
4 2
4.8%
3 3
7.1%

마약류(대마)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.97619
Minimum0
Maximum1123
Zeros22
Zeros (%)52.4%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:35:03.944370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile14.95
Maximum1123
Range1123
Interquartile range (IQR)2

Descriptive statistics

Standard deviation172.98435
Coefficient of variation (CV)5.9698789
Kurtosis41.942109
Mean28.97619
Median Absolute Deviation (MAD)0
Skewness6.4742365
Sum1217
Variance29923.585
MonotonicityNot monotonic
2023-12-12T19:35:04.146059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 22
52.4%
1 7
 
16.7%
3 3
 
7.1%
2 3
 
7.1%
5 1
 
2.4%
15 1
 
2.4%
10 1
 
2.4%
17 1
 
2.4%
14 1
 
2.4%
1123 1
 
2.4%
ValueCountFrequency (%)
0 22
52.4%
1 7
 
16.7%
2 3
 
7.1%
3 3
 
7.1%
5 1
 
2.4%
10 1
 
2.4%
11 1
 
2.4%
14 1
 
2.4%
15 1
 
2.4%
17 1
 
2.4%
ValueCountFrequency (%)
1123 1
 
2.4%
17 1
 
2.4%
15 1
 
2.4%
14 1
 
2.4%
11 1
 
2.4%
10 1
 
2.4%
5 1
 
2.4%
3 3
7.1%
2 3
7.1%
1 7
16.7%

마약류(향정신성의약품)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.14286
Minimum0
Maximum3874
Zeros13
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:35:04.295510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile44.8
Maximum3874
Range3874
Interquartile range (IQR)6

Descriptive statistics

Standard deviation596.68248
Coefficient of variation (CV)5.9583129
Kurtosis41.95134
Mean100.14286
Median Absolute Deviation (MAD)2
Skewness6.4752665
Sum4206
Variance356029.98
MonotonicityNot monotonic
2023-12-12T19:35:04.446352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 13
31.0%
1 6
14.3%
2 6
14.3%
6 3
 
7.1%
3 3
 
7.1%
24 2
 
4.8%
33 1
 
2.4%
19 1
 
2.4%
4 1
 
2.4%
10 1
 
2.4%
Other values (5) 5
 
11.9%
ValueCountFrequency (%)
0 13
31.0%
1 6
14.3%
2 6
14.3%
3 3
 
7.1%
4 1
 
2.4%
6 3
 
7.1%
10 1
 
2.4%
19 1
 
2.4%
24 2
 
4.8%
33 1
 
2.4%
ValueCountFrequency (%)
3874 1
 
2.4%
53 1
 
2.4%
45 1
 
2.4%
41 1
 
2.4%
34 1
 
2.4%
33 1
 
2.4%
24 2
4.8%
19 1
 
2.4%
10 1
 
2.4%
6 3
7.1%

본드신나등
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7142857
Minimum0
Maximum133
Zeros28
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:35:04.571841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3.9
Maximum133
Range133
Interquartile range (IQR)1

Descriptive statistics

Standard deviation20.469234
Coefficient of variation (CV)5.5109477
Kurtosis41.705354
Mean3.7142857
Median Absolute Deviation (MAD)0
Skewness6.4480444
Sum156
Variance418.98955
MonotonicityNot monotonic
2023-12-12T19:35:04.713184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 28
66.7%
1 9
 
21.4%
2 2
 
4.8%
4 1
 
2.4%
6 1
 
2.4%
133 1
 
2.4%
ValueCountFrequency (%)
0 28
66.7%
1 9
 
21.4%
2 2
 
4.8%
4 1
 
2.4%
6 1
 
2.4%
133 1
 
2.4%
ValueCountFrequency (%)
133 1
 
2.4%
6 1
 
2.4%
4 1
 
2.4%
2 2
 
4.8%
1 9
 
21.4%
0 28
66.7%

알코올
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3041.7857
Minimum0
Maximum60665
Zeros9
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:35:05.202847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.5
median101
Q31811.75
95-th percentile10822.5
Maximum60665
Range60665
Interquartile range (IQR)1805.25

Descriptive statistics

Standard deviation9944.9145
Coefficient of variation (CV)3.269433
Kurtosis29.095397
Mean3041.7857
Median Absolute Deviation (MAD)101
Skewness5.1922495
Sum127755
Variance98901325
MonotonicityNot monotonic
2023-12-12T19:35:05.367235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 9
 
21.4%
2823 1
 
2.4%
382 1
 
2.4%
20 1
 
2.4%
1623 1
 
2.4%
186 1
 
2.4%
825 1
 
2.4%
443 1
 
2.4%
2591 1
 
2.4%
3842 1
 
2.4%
Other values (24) 24
57.1%
ValueCountFrequency (%)
0 9
21.4%
1 1
 
2.4%
6 1
 
2.4%
8 1
 
2.4%
10 1
 
2.4%
16 1
 
2.4%
20 1
 
2.4%
29 1
 
2.4%
30 1
 
2.4%
34 1
 
2.4%
ValueCountFrequency (%)
60665 1
2.4%
23262 1
2.4%
11122 1
2.4%
5132 1
2.4%
4736 1
2.4%
3842 1
2.4%
2823 1
2.4%
2591 1
2.4%
1944 1
2.4%
1909 1
2.4%

해당무
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30244.429
Minimum28
Maximum184308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:35:05.568878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile78.6
Q1603.75
median4415.5
Q325803
95-th percentile160800.35
Maximum184308
Range184280
Interquartile range (IQR)25199.25

Descriptive statistics

Standard deviation52314.783
Coefficient of variation (CV)1.7297329
Kurtosis2.7041529
Mean30244.429
Median Absolute Deviation (MAD)4356
Skewness1.9889505
Sum1270266
Variance2.7368365 × 109
MonotonicityNot monotonic
2023-12-12T19:35:05.780279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
17634 1
 
2.4%
44996 1
 
2.4%
6887 1
 
2.4%
43 1
 
2.4%
128792 1
 
2.4%
20811 1
 
2.4%
2265 1
 
2.4%
30236 1
 
2.4%
11216 1
 
2.4%
19020 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
28 1
2.4%
43 1
2.4%
76 1
2.4%
128 1
2.4%
172 1
2.4%
230 1
2.4%
258 1
2.4%
326 1
2.4%
480 1
2.4%
523 1
2.4%
ValueCountFrequency (%)
184308 1
2.4%
162908 1
2.4%
160866 1
2.4%
159553 1
2.4%
128792 1
2.4%
86759 1
2.4%
86389 1
2.4%
44996 1
2.4%
34492 1
2.4%
30236 1
2.4%

미상
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18418.833
Minimum58
Maximum146244
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:35:06.015567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum58
5-th percentile74.15
Q1245
median1802.5
Q37342.5
95-th percentile114115
Maximum146244
Range146186
Interquartile range (IQR)7097.5

Descriptive statistics

Standard deviation39248.777
Coefficient of variation (CV)2.1309046
Kurtosis4.2008575
Mean18418.833
Median Absolute Deviation (MAD)1727
Skewness2.336068
Sum773591
Variance1.5404665 × 109
MonotonicityNot monotonic
2023-12-12T19:35:06.213725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
6461 1
 
2.4%
20305 1
 
2.4%
8553 1
 
2.4%
74 1
 
2.4%
114523 1
 
2.4%
14929 1
 
2.4%
6059 1
 
2.4%
3605 1
 
2.4%
1884 1
 
2.4%
1721 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
58 1
2.4%
60 1
2.4%
74 1
2.4%
77 1
2.4%
82 1
2.4%
91 1
2.4%
116 1
2.4%
133 1
2.4%
147 1
2.4%
148 1
2.4%
ValueCountFrequency (%)
146244 1
2.4%
134869 1
2.4%
114523 1
2.4%
106363 1
2.4%
80540 1
2.4%
76276 1
2.4%
20305 1
2.4%
14929 1
2.4%
10238 1
2.4%
8553 1
2.4%

Interactions

2023-12-12T19:35:00.896954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:51.892425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:53.139950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:54.348797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:55.426497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:56.448230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:57.728665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:58.944271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:59.879821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:35:01.035655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:52.038157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:53.271129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:54.470399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:55.523768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:56.546367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:57.823620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:59.083040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:59.987178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:35:01.140925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:52.168917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:53.373978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:54.594828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:55.614138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:56.664879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:57.925119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:59.180400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:35:00.087051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:35:01.246677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:52.293785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:53.497186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:54.731199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:55.699867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:56.787705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:58.021201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:59.276159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:35:00.202202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:35:01.358676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:52.436385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:53.601756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:54.836080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:55.821926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:56.883993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:58.142795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:59.406174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:35:00.314140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:35:01.480252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:52.578693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:53.756732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:54.968930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:55.954088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:57.329779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:58.297539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:59.514565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:35:00.422698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:35:01.585752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:52.711628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:53.876492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:55.099676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:56.057174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:57.417262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:58.438802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:59.607088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:35:00.544561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:35:01.690518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:52.846932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:54.030276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:55.211027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:56.212916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:57.512015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:58.585907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:59.696426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:35:00.653747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:35:01.813102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:53.009857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:54.200664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:55.341327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:56.345348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:57.620643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:58.763816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:34:59.792164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:35:00.763895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:35:06.359499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
범죄대분류범죄중분류계마약류(소계)마약류(마약)마약류(대마)마약류(향정신성의약품)본드신나등알코올해당무미상
범죄대분류1.0000.0000.7831.0001.0001.0001.0001.0000.4940.7000.664
범죄중분류0.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
계0.7830.0001.0000.0000.0000.0000.0000.5900.9210.9600.841
마약류(소계)1.0000.0000.0001.0000.6710.6710.6710.0000.0000.0000.000
마약류(마약)1.0000.0000.0000.6711.0000.6710.6710.0000.0000.0000.000
마약류(대마)1.0000.0000.0000.6710.6711.0000.6710.0000.0000.0000.000
마약류(향정신성의약품)1.0000.0000.0000.6710.6710.6711.0000.0000.0000.0000.000
본드신나등1.0000.0000.5900.0000.0000.0000.0001.0000.0000.4210.544
알코올0.4940.0000.9210.0000.0000.0000.0000.0001.0000.8220.719
해당무0.7000.0000.9600.0000.0000.0000.0000.4210.8221.0000.861
미상0.6640.0000.8410.0000.0000.0000.0000.5440.7190.8611.000
2023-12-12T19:35:06.542751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계마약류(소계)마약류(마약)마약류(대마)마약류(향정신성의약품)본드신나등알코올해당무미상범죄대분류
계1.0000.7840.6330.8260.7700.5050.8330.9790.9580.434
마약류(소계)0.7841.0000.9130.8300.9710.5300.7690.7920.7290.822
마약류(마약)0.6330.9131.0000.6840.8650.5170.6210.6270.6300.822
마약류(대마)0.8260.8300.6841.0000.7820.3850.6710.8340.7850.822
마약류(향정신성의약품)0.7700.9710.8650.7821.0000.4830.7400.7730.7200.822
본드신나등0.5050.5300.5170.3850.4831.0000.6970.5050.4740.822
알코올0.8330.7690.6210.6710.7400.6971.0000.8480.7540.238
해당무0.9790.7920.6270.8340.7730.5050.8481.0000.9030.348
미상0.9580.7290.6300.7850.7200.4740.7540.9031.0000.282
범죄대분류0.4340.8220.8220.8220.8220.8220.2380.3480.2821.000

Missing values

2023-12-12T19:35:01.997100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:35:02.229641image/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

범죄대분류범죄중분류계마약류(소계)마약류(마약)마약류(대마)마약류(향정신성의약품)본드신나등알코올해당무미상
0강력범죄소계26971491513342823176346461
1강력범죄살인기수341320111623091
2강력범죄살인미수등454210104432682
3강력범죄강도120282060341013147
4강력범죄강간6113244119258432862217
5강력범죄유사강간9340000090612232
6강력범죄강제추행16382730401909109823484
7강력범죄기타 강간 강제추행등349200202925860
8강력범죄방화119633001117927148
9절도범죄소계98425378524117608638910238
범죄대분류범죄중분류계마약류(소계)마약류(마약)마약류(대마)마약류(향정신성의약품)본드신나등알코올해당무미상
32특별경제범죄소계6790411416125914499620305
33마약범죄소계116305423426112338740651721029
34보건범죄소계21810146260112179073777
35환경범죄소계43290000013659669
36교통범죄소계356446458334160665160866134869
37노동범죄소계613000000480133
38안보범죄소계67800000060177
39선거범죄소계15450000010949586
40병역범죄소계31641001002877286
41기타범죄소계24453990261153133513216290876276