Overview

Dataset statistics

Number of variables12
Number of observations467
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory48.0 KiB
Average record size in memory105.3 B

Variable types

Categorical2
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/15054458/fileData.do

Alerts

범죄대분류 is highly overall correlated with 범죄중분류High correlation
범죄중분류 is highly overall correlated with 범죄대분류High correlation
구속-현행범체포 is highly overall correlated with 구속-긴급체포 and 4 other fieldsHigh correlation
구속-긴급체포 is highly overall correlated with 구속-현행범체포 and 4 other fieldsHigh correlation
구속-사전영장 is highly overall correlated with 구속-현행범체포 and 5 other fieldsHigh correlation
구속-체포 is highly overall correlated with 구속-현행범체포 and 5 other fieldsHigh correlation
불구속-불구속입건 is highly overall correlated with 구속-사전영장 and 3 other fieldsHigh correlation
불구속-검사기각 is highly overall correlated with 구속-현행범체포 and 5 other fieldsHigh correlation
불구속-판사기각 is highly overall correlated with 구속-현행범체포 and 5 other fieldsHigh correlation
구속-현행범체포 has 293 (62.7%) zerosZeros
구속-긴급체포 has 273 (58.5%) zerosZeros
구속-사전영장 has 239 (51.2%) zerosZeros
구속-체포 has 254 (54.4%) zerosZeros
불구속-불구속입건 has 78 (16.7%) zerosZeros
불구속-검사기각 has 302 (64.7%) zerosZeros
불구속-판사기각 has 282 (60.4%) zerosZeros
불구속-적부심석방 has 414 (88.7%) zerosZeros
불구속-검사구속취소 has 431 (92.3%) zerosZeros

Reproduction

Analysis started2023-12-12 22:37:40.506387
Analysis finished2023-12-12 22:37:50.042215
Duration9.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄대분류
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
기타범죄
127 
강력범죄
100 
폭력범죄
89 
지능범죄
48 
특별경제범죄
23 
Other values (10)
80 

Length

Max length6
Median length4
Mean length4.0985011
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타범죄 127
27.2%
강력범죄 100
21.4%
폭력범죄 89
19.1%
지능범죄 48
 
10.3%
특별경제범죄 23
 
4.9%
풍속범죄 17
 
3.6%
교통범죄 12
 
2.6%
절도범죄 10
 
2.1%
보건범죄 10
 
2.1%
환경범죄 9
 
1.9%
Other values (5) 22
 
4.7%

Length

2023-12-13T07:37:50.142015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타범죄 127
27.2%
강력범죄 100
21.4%
폭력범죄 89
19.1%
지능범죄 48
 
10.3%
특별경제범죄 23
 
4.9%
풍속범죄 17
 
3.6%
교통범죄 12
 
2.6%
절도범죄 10
 
2.1%
보건범죄 10
 
2.1%
환경범죄 9
 
1.9%
Other values (5) 22
 
4.7%

범죄중분류
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
기타범죄
127 
특별경제범죄
 
23
강제추행
 
19
강간
 
18
폭행
 
17
Other values (33)
263 

Length

Max length11
Median length4
Mean length3.8736617
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타범죄 127
27.2%
특별경제범죄 23
 
4.9%
강제추행 19
 
4.1%
강간 18
 
3.9%
폭행 17
 
3.6%
약취·유인 16
 
3.4%
상해 16
 
3.4%
방화 15
 
3.2%
유사강간 12
 
2.6%
강도 12
 
2.6%
Other values (28) 192
41.1%

Length

2023-12-13T07:37:50.260415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타범죄 127
26.6%
특별경제범죄 23
 
4.8%
강제추행 19
 
4.0%
강간 18
 
3.8%
폭행 17
 
3.6%
약취·유인 16
 
3.3%
상해 16
 
3.3%
방화 15
 
3.1%
강도 12
 
2.5%
교통범죄 12
 
2.5%
Other values (29) 203
42.5%
Distinct461
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2023-12-13T07:37:50.459275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length27
Mean length10.055675
Min length2

Characters and Unicode

Total characters4696
Distinct characters284
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique455 ?
Unique (%)97.4%

Sample

1st row살인
2nd row영아살해
3rd row존속살해
4th row촉탁·승낙살인
5th row자살교사·방조
ValueCountFrequency (%)
7
 
1.4%
살인 2
 
0.4%
존속살해 2
 
0.4%
촉탁·승낙살인 2
 
0.4%
흉기등 2
 
0.4%
독직폭행·가혹행위 2
 
0.4%
영아살해 2
 
0.4%
위계·위력·촉탁·승낙살인 2
 
0.4%
자살교사·방조 2
 
0.4%
직업안정법 1
 
0.2%
Other values (470) 470
95.1%
2023-12-13T07:37:50.785712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
235
 
5.0%
( 177
 
3.8%
) 177
 
3.8%
· 130
 
2.8%
117
 
2.5%
108
 
2.3%
103
 
2.2%
101
 
2.2%
90
 
1.9%
89
 
1.9%
Other values (274) 3369
71.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4175
88.9%
Open Punctuation 178
 
3.8%
Close Punctuation 178
 
3.8%
Other Punctuation 130
 
2.8%
Space Separator 27
 
0.6%
Decimal Number 8
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
235
 
5.6%
117
 
2.8%
108
 
2.6%
103
 
2.5%
101
 
2.4%
90
 
2.2%
89
 
2.1%
88
 
2.1%
79
 
1.9%
79
 
1.9%
Other values (266) 3086
73.9%
Open Punctuation
ValueCountFrequency (%)
( 177
99.4%
[ 1
 
0.6%
Close Punctuation
ValueCountFrequency (%)
) 177
99.4%
] 1
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 4
50.0%
3 4
50.0%
Other Punctuation
ValueCountFrequency (%)
· 130
100.0%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4175
88.9%
Common 521
 
11.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
235
 
5.6%
117
 
2.8%
108
 
2.6%
103
 
2.5%
101
 
2.4%
90
 
2.2%
89
 
2.1%
88
 
2.1%
79
 
1.9%
79
 
1.9%
Other values (266) 3086
73.9%
Common
ValueCountFrequency (%)
( 177
34.0%
) 177
34.0%
· 130
25.0%
27
 
5.2%
1 4
 
0.8%
3 4
 
0.8%
] 1
 
0.2%
[ 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4175
88.9%
ASCII 391
 
8.3%
None 130
 
2.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
235
 
5.6%
117
 
2.8%
108
 
2.6%
103
 
2.5%
101
 
2.4%
90
 
2.2%
89
 
2.1%
88
 
2.1%
79
 
1.9%
79
 
1.9%
Other values (266) 3086
73.9%
ASCII
ValueCountFrequency (%)
( 177
45.3%
) 177
45.3%
27
 
6.9%
1 4
 
1.0%
3 4
 
1.0%
] 1
 
0.3%
[ 1
 
0.3%
None
ValueCountFrequency (%)
· 130
100.0%

구속-현행범체포
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct52
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.226981
Minimum0
Maximum789
Zeros293
Zeros (%)62.7%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-13T07:37:50.911740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile33.4
Maximum789
Range789
Interquartile range (IQR)2

Descriptive statistics

Standard deviation62.343778
Coefficient of variation (CV)5.0988694
Kurtosis93.275497
Mean12.226981
Median Absolute Deviation (MAD)0
Skewness8.9651035
Sum5710
Variance3886.7467
MonotonicityNot monotonic
2023-12-13T07:37:51.315202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 293
62.7%
1 37
 
7.9%
2 22
 
4.7%
3 22
 
4.7%
5 8
 
1.7%
4 7
 
1.5%
9 6
 
1.3%
12 5
 
1.1%
6 5
 
1.1%
22 4
 
0.9%
Other values (42) 58
 
12.4%
ValueCountFrequency (%)
0 293
62.7%
1 37
 
7.9%
2 22
 
4.7%
3 22
 
4.7%
4 7
 
1.5%
5 8
 
1.7%
6 5
 
1.1%
7 4
 
0.9%
8 4
 
0.9%
9 6
 
1.3%
ValueCountFrequency (%)
789 1
0.2%
706 1
0.2%
470 1
0.2%
322 1
0.2%
316 1
0.2%
306 1
0.2%
207 1
0.2%
205 1
0.2%
178 1
0.2%
153 1
0.2%

구속-긴급체포
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.702355
Minimum0
Maximum819
Zeros273
Zeros (%)58.5%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-13T07:37:51.460931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile29
Maximum819
Range819
Interquartile range (IQR)3

Descriptive statistics

Standard deviation62.50383
Coefficient of variation (CV)5.3411324
Kurtosis97.179484
Mean11.702355
Median Absolute Deviation (MAD)0
Skewness9.3026824
Sum5465
Variance3906.7288
MonotonicityNot monotonic
2023-12-13T07:37:51.576360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 273
58.5%
1 50
 
10.7%
2 25
 
5.4%
3 18
 
3.9%
9 9
 
1.9%
7 9
 
1.9%
5 8
 
1.7%
4 7
 
1.5%
14 5
 
1.1%
6 5
 
1.1%
Other values (38) 58
 
12.4%
ValueCountFrequency (%)
0 273
58.5%
1 50
 
10.7%
2 25
 
5.4%
3 18
 
3.9%
4 7
 
1.5%
5 8
 
1.7%
6 5
 
1.1%
7 9
 
1.9%
8 3
 
0.6%
9 9
 
1.9%
ValueCountFrequency (%)
819 1
0.2%
594 1
0.2%
589 1
0.2%
468 1
0.2%
288 1
0.2%
224 1
0.2%
150 1
0.2%
147 1
0.2%
137 1
0.2%
131 1
0.2%

구속-사전영장
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3490364
Minimum0
Maximum686
Zeros239
Zeros (%)51.2%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-13T07:37:51.696630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile42
Maximum686
Range686
Interquartile range (IQR)5

Descriptive statistics

Standard deviation37.941736
Coefficient of variation (CV)4.0583579
Kurtosis220.57724
Mean9.3490364
Median Absolute Deviation (MAD)0
Skewness13.11614
Sum4366
Variance1439.5753
MonotonicityNot monotonic
2023-12-13T07:37:51.824735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 239
51.2%
1 44
 
9.4%
3 25
 
5.4%
2 21
 
4.5%
4 16
 
3.4%
5 9
 
1.9%
8 9
 
1.9%
7 8
 
1.7%
6 7
 
1.5%
11 6
 
1.3%
Other values (49) 83
 
17.8%
ValueCountFrequency (%)
0 239
51.2%
1 44
 
9.4%
2 21
 
4.5%
3 25
 
5.4%
4 16
 
3.4%
5 9
 
1.9%
6 7
 
1.5%
7 8
 
1.7%
8 9
 
1.9%
9 6
 
1.3%
ValueCountFrequency (%)
686 1
0.2%
207 1
0.2%
196 1
0.2%
135 1
0.2%
131 1
0.2%
104 1
0.2%
95 1
0.2%
82 2
0.4%
79 1
0.2%
78 1
0.2%

구속-체포
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.970021
Minimum0
Maximum2228
Zeros254
Zeros (%)54.4%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-13T07:37:52.005621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile47.4
Maximum2228
Range2228
Interquartile range (IQR)5

Descriptive statistics

Standard deviation118.11224
Coefficient of variation (CV)6.9600525
Kurtosis272.81586
Mean16.970021
Median Absolute Deviation (MAD)0
Skewness15.483338
Sum7925
Variance13950.501
MonotonicityNot monotonic
2023-12-13T07:37:52.137394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 254
54.4%
1 32
 
6.9%
2 30
 
6.4%
3 19
 
4.1%
6 10
 
2.1%
4 10
 
2.1%
7 8
 
1.7%
5 7
 
1.5%
8 6
 
1.3%
17 6
 
1.3%
Other values (52) 85
 
18.2%
ValueCountFrequency (%)
0 254
54.4%
1 32
 
6.9%
2 30
 
6.4%
3 19
 
4.1%
4 10
 
2.1%
5 7
 
1.5%
6 10
 
2.1%
7 8
 
1.7%
8 6
 
1.3%
9 4
 
0.9%
ValueCountFrequency (%)
2228 1
0.2%
866 1
0.2%
737 1
0.2%
255 1
0.2%
226 1
0.2%
202 1
0.2%
195 1
0.2%
153 1
0.2%
145 1
0.2%
134 1
0.2%

불구속-불구속입건
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct274
Distinct (%)58.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3895.6017
Minimum0
Maximum255286
Zeros78
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-13T07:37:52.262167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median75
Q3883.5
95-th percentile11917.8
Maximum255286
Range255286
Interquartile range (IQR)880.5

Descriptive statistics

Standard deviation20302.01
Coefficient of variation (CV)5.2115211
Kurtosis95.312293
Mean3895.6017
Median Absolute Deviation (MAD)75
Skewness9.3327368
Sum1819246
Variance4.1217163 × 108
MonotonicityNot monotonic
2023-12-13T07:37:52.418198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78
 
16.7%
1 18
 
3.9%
3 12
 
2.6%
2 11
 
2.4%
6 6
 
1.3%
8 5
 
1.1%
24 5
 
1.1%
7 5
 
1.1%
12 5
 
1.1%
10 5
 
1.1%
Other values (264) 317
67.9%
ValueCountFrequency (%)
0 78
16.7%
1 18
 
3.9%
2 11
 
2.4%
3 12
 
2.6%
4 4
 
0.9%
5 2
 
0.4%
6 6
 
1.3%
7 5
 
1.1%
8 5
 
1.1%
9 2
 
0.4%
ValueCountFrequency (%)
255286 1
0.2%
197424 1
0.2%
192680 1
0.2%
180253 1
0.2%
77436 1
0.2%
57730 1
0.2%
50100 1
0.2%
49277 1
0.2%
36320 1
0.2%
36291 1
0.2%

불구속-검사기각
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5824411
Minimum0
Maximum158
Zeros302
Zeros (%)64.7%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-13T07:37:52.530330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile10
Maximum158
Range158
Interquartile range (IQR)1

Descriptive statistics

Standard deviation10.716957
Coefficient of variation (CV)4.1499328
Kurtosis116.04413
Mean2.5824411
Median Absolute Deviation (MAD)0
Skewness9.6375492
Sum1206
Variance114.85317
MonotonicityNot monotonic
2023-12-13T07:37:52.687072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 302
64.7%
1 53
 
11.3%
2 31
 
6.6%
3 17
 
3.6%
4 10
 
2.1%
6 9
 
1.9%
5 9
 
1.9%
10 6
 
1.3%
7 4
 
0.9%
9 4
 
0.9%
Other values (19) 22
 
4.7%
ValueCountFrequency (%)
0 302
64.7%
1 53
 
11.3%
2 31
 
6.6%
3 17
 
3.6%
4 10
 
2.1%
5 9
 
1.9%
6 9
 
1.9%
7 4
 
0.9%
8 3
 
0.6%
9 4
 
0.9%
ValueCountFrequency (%)
158 1
0.2%
102 1
0.2%
72 1
0.2%
55 1
0.2%
47 1
0.2%
46 1
0.2%
37 1
0.2%
31 1
0.2%
25 1
0.2%
24 1
0.2%

불구속-판사기각
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6445396
Minimum0
Maximum328
Zeros282
Zeros (%)60.4%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-13T07:37:52.812370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile16.7
Maximum328
Range328
Interquartile range (IQR)1

Descriptive statistics

Standard deviation22.608679
Coefficient of variation (CV)4.8677976
Kurtosis127.0655
Mean4.6445396
Median Absolute Deviation (MAD)0
Skewness10.42185
Sum2169
Variance511.15235
MonotonicityNot monotonic
2023-12-13T07:37:52.930940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 282
60.4%
1 71
 
15.2%
2 21
 
4.5%
3 15
 
3.2%
4 12
 
2.6%
6 8
 
1.7%
10 7
 
1.5%
7 5
 
1.1%
16 4
 
0.9%
12 4
 
0.9%
Other values (26) 38
 
8.1%
ValueCountFrequency (%)
0 282
60.4%
1 71
 
15.2%
2 21
 
4.5%
3 15
 
3.2%
4 12
 
2.6%
5 3
 
0.6%
6 8
 
1.7%
7 5
 
1.1%
8 2
 
0.4%
9 4
 
0.9%
ValueCountFrequency (%)
328 1
0.2%
255 1
0.2%
145 1
0.2%
136 1
0.2%
91 1
0.2%
63 1
0.2%
56 1
0.2%
52 1
0.2%
50 1
0.2%
38 1
0.2%

불구속-적부심석방
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30620985
Minimum0
Maximum25
Zeros414
Zeros (%)88.7%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-13T07:37:53.032991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5785662
Coefficient of variation (CV)5.1551777
Kurtosis144.29111
Mean0.30620985
Median Absolute Deviation (MAD)0
Skewness10.696292
Sum143
Variance2.4918712
MonotonicityNot monotonic
2023-12-13T07:37:53.158282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 414
88.7%
1 31
 
6.6%
2 7
 
1.5%
3 7
 
1.5%
5 4
 
0.9%
9 2
 
0.4%
14 1
 
0.2%
25 1
 
0.2%
ValueCountFrequency (%)
0 414
88.7%
1 31
 
6.6%
2 7
 
1.5%
3 7
 
1.5%
5 4
 
0.9%
9 2
 
0.4%
14 1
 
0.2%
25 1
 
0.2%
ValueCountFrequency (%)
25 1
 
0.2%
14 1
 
0.2%
9 2
 
0.4%
5 4
 
0.9%
3 7
 
1.5%
2 7
 
1.5%
1 31
 
6.6%
0 414
88.7%

불구속-검사구속취소
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15203426
Minimum0
Maximum7
Zeros431
Zeros (%)92.3%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-13T07:37:53.274840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.67798864
Coefficient of variation (CV)4.4594464
Kurtosis46.392813
Mean0.15203426
Median Absolute Deviation (MAD)0
Skewness6.2767312
Sum71
Variance0.4596686
MonotonicityNot monotonic
2023-12-13T07:37:53.401066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 431
92.3%
1 21
 
4.5%
2 7
 
1.5%
4 3
 
0.6%
3 2
 
0.4%
5 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
ValueCountFrequency (%)
0 431
92.3%
1 21
 
4.5%
2 7
 
1.5%
3 2
 
0.4%
4 3
 
0.6%
5 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
ValueCountFrequency (%)
7 1
 
0.2%
6 1
 
0.2%
5 1
 
0.2%
4 3
 
0.6%
3 2
 
0.4%
2 7
 
1.5%
1 21
 
4.5%
0 431
92.3%

Interactions

2023-12-13T07:37:48.779622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:41.182321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:42.261673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:43.121640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:43.977429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:45.138204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:46.114568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:47.021319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:47.894569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:48.890382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:41.333890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:42.365892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:43.218924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:44.089106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:45.256535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:46.234478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:47.109487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:47.997510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:48.992461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:41.443207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:42.458047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:43.308077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:44.178132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:45.369456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:46.327157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:47.208582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:48.078805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:49.111110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:41.561343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:42.540867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:43.403775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:44.278135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:45.503568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:46.420912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:47.305490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:48.194320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:49.213245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:41.671076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:42.640466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:43.500926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:44.635142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:45.596195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:46.513343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:47.386567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:48.280173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:49.311785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:41.784500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:42.732793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:43.597910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:44.718972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:45.692977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:46.615474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:47.480224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:48.360815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:49.423226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:41.926351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:42.842860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:43.700469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:44.846040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:45.820288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:46.723916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:47.581427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:48.463403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:49.523674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:42.033455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:42.936515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:43.789500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:44.929447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:45.906057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:46.818117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:47.689954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:48.582624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:49.628192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:42.147085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:43.032092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:43.884316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:45.030103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:46.016760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:46.918837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:47.785405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:37:48.686155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:37:53.525266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
범죄대분류범죄중분류구속-현행범체포구속-긴급체포구속-사전영장구속-체포불구속-불구속입건불구속-검사기각불구속-판사기각불구속-적부심석방불구속-검사구속취소
범죄대분류1.0001.0000.1380.4700.2940.3500.3440.2200.2850.3530.318
범죄중분류1.0001.0000.0000.3500.1600.3980.3500.0000.0000.4660.159
구속-현행범체포0.1380.0001.0000.7190.8610.9810.8820.9130.9010.7860.880
구속-긴급체포0.4700.3500.7191.0000.6130.8410.7430.9240.8090.6340.788
구속-사전영장0.2940.1600.8610.6131.0000.7030.8650.8700.8250.8710.786
구속-체포0.3500.3980.9810.8410.7031.0000.8420.8370.8950.7930.967
불구속-불구속입건0.3440.3500.8820.7430.8650.8421.0000.9210.9540.9770.844
불구속-검사기각0.2200.0000.9130.9240.8700.8370.9211.0000.9340.8310.807
불구속-판사기각0.2850.0000.9010.8090.8250.8950.9540.9341.0000.9500.867
불구속-적부심석방0.3530.4660.7860.6340.8710.7930.9770.8310.9501.0000.851
불구속-검사구속취소0.3180.1590.8800.7880.7860.9670.8440.8070.8670.8511.000
2023-12-13T07:37:53.688571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
범죄대분류범죄중분류
범죄대분류1.0000.974
범죄중분류0.9741.000
2023-12-13T07:37:53.783244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구속-현행범체포구속-긴급체포구속-사전영장구속-체포불구속-불구속입건불구속-검사기각불구속-판사기각불구속-적부심석방불구속-검사구속취소범죄대분류범죄중분류
구속-현행범체포1.0000.6560.6820.6710.4740.5730.6520.3920.2730.0580.000
구속-긴급체포0.6561.0000.7060.7490.4120.5820.6380.3530.1930.2320.145
구속-사전영장0.6820.7061.0000.7970.6020.6770.7300.4120.2920.1280.071
구속-체포0.6710.7490.7971.0000.6000.6840.7440.4110.2920.2020.204
불구속-불구속입건0.4740.4120.6020.6001.0000.6700.6510.4420.4170.1660.154
불구속-검사기각0.5730.5820.6770.6840.6701.0000.7190.4820.3870.1000.000
불구속-판사기각0.6520.6380.7300.7440.6510.7191.0000.4530.3530.1350.000
불구속-적부심석방0.3920.3530.4120.4110.4420.4820.4531.0000.3830.1500.183
불구속-검사구속취소0.2730.1930.2920.2920.4170.3870.3530.3831.0000.1400.058
범죄대분류0.0580.2320.1280.2020.1660.1000.1350.1500.1401.0000.974
범죄중분류0.0000.1450.0710.2040.1540.0000.0000.1830.0580.9741.000

Missing values

2023-12-13T07:37:49.767957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:37:49.950822image/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

범죄대분류범죄중분류범죄소분류구속-현행범체포구속-긴급체포구속-사전영장구속-체포불구속-불구속입건불구속-검사기각불구속-판사기각불구속-적부심석방불구속-검사구속취소
0강력범죄살인기수살인521201014910100
1강력범죄살인기수영아살해021080000
2강력범죄살인기수존속살해7140230000
3강력범죄살인기수촉탁·승낙살인000010000
4강력범죄살인기수자살교사·방조0110241100
5강력범죄살인기수위계·위력·촉탁·승낙살인000000000
6강력범죄살인기수특가법(보복살인등)000000000
7강력범죄살인미수등살인20513718191216610
8강력범죄살인미수등영아살해000000000
9강력범죄살인미수등존속살해951071100
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