Overview

Dataset statistics

Number of variables9
Number of observations473
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.2 KiB
Average record size in memory78.3 B

Variable types

Categorical2
Text1
Numeric6

Dataset

Description경찰청 범죄발생 및 검거현황 범죄대분류, 범죄중분류, 범죄소분류, 발생, 검거, 검거인원(남,여,불상), 법인체 강력범죄, 폭력범죄, 절도범죄, 지능범죄, 풍속범죄, 특별경제범등
Author경찰청
URLhttps://www.data.go.kr/data/15064217/fileData.do

Alerts

발생 is highly overall correlated with 검거 and 4 other fieldsHigh correlation
검거 is highly overall correlated with 발생 and 4 other fieldsHigh correlation
검거인원(남) is highly overall correlated with 발생 and 4 other fieldsHigh correlation
검거인원(여) is highly overall correlated with 발생 and 4 other fieldsHigh correlation
불상 is highly overall correlated with 발생 and 4 other fieldsHigh correlation
법인체 is highly overall correlated with 발생 and 4 other fieldsHigh correlation
범죄대분류 is highly overall correlated with 범죄중분류High correlation
범죄중분류 is highly overall correlated with 범죄대분류High correlation
불상 is highly skewed (γ1 = 20.23999327)Skewed
발생 has 62 (13.1%) zerosZeros
검거 has 64 (13.5%) zerosZeros
검거인원(남) has 67 (14.2%) zerosZeros
검거인원(여) has 128 (27.1%) zerosZeros
불상 has 244 (51.6%) zerosZeros
법인체 has 325 (68.7%) zerosZeros

Reproduction

Analysis started2023-12-12 22:30:56.563514
Analysis finished2023-12-12 22:31:01.465504
Duration4.9 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
129 
강력범죄
102 
폭력범죄
89 
지능범죄
48 
특별경제범죄
23 
Other values (10)
82 

Length

Max length6
Median length4
Mean length4.0972516
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타범죄 129
27.3%
강력범죄 102
21.6%
폭력범죄 89
18.8%
지능범죄 48
 
10.1%
특별경제범죄 23
 
4.9%
풍속범죄 19
 
4.0%
교통범죄 13
 
2.7%
절도범죄 10
 
2.1%
보건범죄 10
 
2.1%
환경범죄 8
 
1.7%
Other values (5) 22
 
4.7%

Length

2023-12-13T07:31:01.565228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타범죄 129
27.3%
강력범죄 102
21.6%
폭력범죄 89
18.8%
지능범죄 48
 
10.1%
특별경제범죄 23
 
4.9%
풍속범죄 19
 
4.0%
교통범죄 13
 
2.7%
절도범죄 10
 
2.1%
보건범죄 10
 
2.1%
환경범죄 8
 
1.7%
Other values (5) 22
 
4.7%

범죄중분류
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
계
202 
강제추행
 
20
강간
 
19
폭행
 
17
상해
 
16
Other values (25)
199 

Length

Max length11
Median length6
Mean length2.4968288
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
계 202
42.7%
강제추행 20
 
4.2%
강간 19
 
4.0%
폭행 17
 
3.6%
상해 16
 
3.4%
약취·유인 16
 
3.4%
방화 15
 
3.2%
성풍속범죄 14
 
3.0%
강도 12
 
2.5%
유사강간 12
 
2.5%
Other values (20) 130
27.5%

Length

2023-12-13T07:31:01.716042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
계 202
41.8%
강제추행 20
 
4.1%
강간 19
 
3.9%
폭행 17
 
3.5%
상해 16
 
3.3%
약취·유인 16
 
3.3%
방화 15
 
3.1%
성풍속범죄 14
 
2.9%
강도 12
 
2.5%
유사강간 12
 
2.5%
Other values (21) 140
29.0%

범죄소분류
Text

Distinct467
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2023-12-13T07:31:01.942725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length28
Mean length10.10148
Min length2

Characters and Unicode

Total characters4778
Distinct characters292
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

Unique461 ?
Unique (%)97.5%

Sample

1st row살인
2nd row영아살해
3rd row존속살해
4th row촉탁·승낙살인
5th row자살교사·방조
ValueCountFrequency (%)
등 6
 
1.2%
살인 2
 
0.4%
자살교사·방조 2
 
0.4%
위계·위력·촉탁·승낙살인 2
 
0.4%
영아살해 2
 
0.4%
존속살해 2
 
0.4%
촉탁·승낙살인 2
 
0.4%
특가법(어린이보호구역치사상 1
 
0.2%
특가법(도주차량 1
 
0.2%
도로교통법(사고후미조치 1
 
0.2%
Other values (469) 469
95.7%
2023-12-13T07:31:02.331906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
법 239
 
5.0%
) 178
 
3.7%
( 178
 
3.7%
· 130
 
2.7%
위 117
 
2.4%
등 111
 
2.3%
상 105
 
2.2%
관 104
 
2.2%
에 91
 
1.9%
한 90
 
1.9%
Other values (282) 3435
71.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4248
88.9%
Close Punctuation 179
 
3.7%
Open Punctuation 179
 
3.7%
Other Punctuation 143
 
3.0%
Space Separator 17
 
0.4%
Decimal Number 12
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
법 239
 
5.6%
위 117
 
2.8%
등 111
 
2.6%
상 105
 
2.5%
관 104
 
2.4%
에 91
 
2.1%
한 90
 
2.1%
특 88
 
2.1%
행 78
 
1.8%
사 78
 
1.8%
Other values (271) 3147
74.1%
Other Punctuation
ValueCountFrequency (%)
· 130
90.9%
, 12
 
8.4%
. 1
 
0.7%
Decimal Number
ValueCountFrequency (%)
1 6
50.0%
3 4
33.3%
6 2
 
16.7%
Close Punctuation
ValueCountFrequency (%)
) 178
99.4%
] 1
 
0.6%
Open Punctuation
ValueCountFrequency (%)
( 178
99.4%
[ 1
 
0.6%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4248
88.9%
Common 530
 
11.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
법 239
 
5.6%
위 117
 
2.8%
등 111
 
2.6%
상 105
 
2.5%
관 104
 
2.4%
에 91
 
2.1%
한 90
 
2.1%
특 88
 
2.1%
행 78
 
1.8%
사 78
 
1.8%
Other values (271) 3147
74.1%
Common
ValueCountFrequency (%)
) 178
33.6%
( 178
33.6%
· 130
24.5%
17
 
3.2%
, 12
 
2.3%
1 6
 
1.1%
3 4
 
0.8%
6 2
 
0.4%
] 1
 
0.2%
[ 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4248
88.9%
ASCII 400
 
8.4%
None 130
 
2.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
법 239
 
5.6%
위 117
 
2.8%
등 111
 
2.6%
상 105
 
2.5%
관 104
 
2.4%
에 91
 
2.1%
한 90
 
2.1%
특 88
 
2.1%
행 78
 
1.8%
사 78
 
1.8%
Other values (271) 3147
74.1%
ASCII
ValueCountFrequency (%)
) 178
44.5%
( 178
44.5%
17
 
4.2%
, 12
 
3.0%
1 6
 
1.5%
3 4
 
1.0%
6 2
 
0.5%
] 1
 
0.2%
[ 1
 
0.2%
. 1
 
0.2%
None
ValueCountFrequency (%)
· 130
100.0%

발생
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct271
Distinct (%)57.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3022.8879
Minimum0
Maximum269825
Zeros62
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-13T07:31:02.487788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median70
Q3677
95-th percentile8754.2
Maximum269825
Range269825
Interquartile range (IQR)673

Descriptive statistics

Standard deviation17450.528
Coefficient of variation (CV)5.7728002
Kurtosis140.72264
Mean3022.8879
Median Absolute Deviation (MAD)70
Skewness10.988131
Sum1429826
Variance3.0452093 × 108
MonotonicityNot monotonic
2023-12-13T07:31:02.664232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 62
 
13.1%
1 24
 
5.1%
2 20
 
4.2%
3 12
 
2.5%
14 8
 
1.7%
4 7
 
1.5%
8 7
 
1.5%
7 7
 
1.5%
5 5
 
1.1%
9 5
 
1.1%
Other values (261) 316
66.8%
ValueCountFrequency (%)
0 62
13.1%
1 24
 
5.1%
2 20
 
4.2%
3 12
 
2.5%
4 7
 
1.5%
5 5
 
1.1%
6 4
 
0.8%
7 7
 
1.5%
8 7
 
1.5%
9 5
 
1.1%
ValueCountFrequency (%)
269825 1
0.2%
155597 1
0.2%
148196 1
0.2%
100137 1
0.2%
91032 1
0.2%
54188 1
0.2%
36419 1
0.2%
30814 1
0.2%
30119 1
0.2%
27826 1
0.2%

검거
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct255
Distinct (%)53.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2403.0973
Minimum0
Maximum170980
Zeros64
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-13T07:31:02.837200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median57
Q3523
95-th percentile7107
Maximum170980
Range170980
Interquartile range (IQR)520

Descriptive statistics

Standard deviation13094.895
Coefficient of variation (CV)5.4491739
Kurtosis107.03937
Mean2403.0973
Median Absolute Deviation (MAD)57
Skewness9.8301155
Sum1136665
Variance1.7147627 × 108
MonotonicityNot monotonic
2023-12-13T07:31:03.318256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 64
 
13.5%
1 30
 
6.3%
2 20
 
4.2%
6 11
 
2.3%
4 9
 
1.9%
3 8
 
1.7%
35 5
 
1.1%
23 5
 
1.1%
16 5
 
1.1%
57 5
 
1.1%
Other values (245) 311
65.8%
ValueCountFrequency (%)
0 64
13.5%
1 30
6.3%
2 20
 
4.2%
3 8
 
1.7%
4 9
 
1.9%
5 4
 
0.8%
6 11
 
2.3%
7 1
 
0.2%
8 5
 
1.1%
9 4
 
0.8%
ValueCountFrequency (%)
170980 1
0.2%
152856 1
0.2%
94934 1
0.2%
90337 1
0.2%
87672 1
0.2%
33312 1
0.2%
28879 1
0.2%
27064 1
0.2%
22600 1
0.2%
21803 1
0.2%

검거인원(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct260
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2119.1966
Minimum0
Maximum125221
Zeros67
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-13T07:31:03.440408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median66
Q3513
95-th percentile7647
Maximum125221
Range125221
Interquartile range (IQR)509

Descriptive statistics

Standard deviation10635.876
Coefficient of variation (CV)5.0188247
Kurtosis93.952559
Mean2119.1966
Median Absolute Deviation (MAD)66
Skewness9.2516841
Sum1002380
Variance1.1312187 × 108
MonotonicityNot monotonic
2023-12-13T07:31:03.576012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67
 
14.2%
1 27
 
5.7%
2 16
 
3.4%
4 9
 
1.9%
5 6
 
1.3%
3 6
 
1.3%
7 6
 
1.3%
18 6
 
1.3%
24 5
 
1.1%
21 5
 
1.1%
Other values (250) 320
67.7%
ValueCountFrequency (%)
0 67
14.2%
1 27
5.7%
2 16
 
3.4%
3 6
 
1.3%
4 9
 
1.9%
5 6
 
1.3%
6 2
 
0.4%
7 6
 
1.3%
8 2
 
0.4%
9 3
 
0.6%
ValueCountFrequency (%)
125221 1
0.2%
121526 1
0.2%
102185 1
0.2%
81250 1
0.2%
47331 1
0.2%
30406 1
0.2%
29379 1
0.2%
21877 1
0.2%
20798 1
0.2%
18029 1
0.2%

검거인원(여)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct172
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean589.48203
Minimum0
Maximum38280
Zeros128
Zeros (%)27.1%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-13T07:31:03.728472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q389
95-th percentile2323.8
Maximum38280
Range38280
Interquartile range (IQR)89

Descriptive statistics

Standard deviation3021.5235
Coefficient of variation (CV)5.1257263
Kurtosis102.58746
Mean589.48203
Median Absolute Deviation (MAD)7
Skewness9.5597182
Sum278825
Variance9129604.5
MonotonicityNot monotonic
2023-12-13T07:31:03.879513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 128
27.1%
1 36
 
7.6%
2 20
 
4.2%
3 18
 
3.8%
4 14
 
3.0%
5 11
 
2.3%
11 9
 
1.9%
9 7
 
1.5%
13 6
 
1.3%
14 6
 
1.3%
Other values (162) 218
46.1%
ValueCountFrequency (%)
0 128
27.1%
1 36
 
7.6%
2 20
 
4.2%
3 18
 
3.8%
4 14
 
3.0%
5 11
 
2.3%
6 5
 
1.1%
7 5
 
1.1%
8 6
 
1.3%
9 7
 
1.5%
ValueCountFrequency (%)
38280 1
0.2%
35358 1
0.2%
25702 1
0.2%
21740 1
0.2%
10540 1
0.2%
9532 1
0.2%
9191 1
0.2%
5849 1
0.2%
5789 1
0.2%
5038 1
0.2%

불상
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct97
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.44186
Minimum0
Maximum43082
Zeros244
Zeros (%)51.6%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-13T07:31:04.023736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311
95-th percentile305.2
Maximum43082
Range43082
Interquartile range (IQR)11

Descriptive statistics

Standard deviation2029.4227
Coefficient of variation (CV)12.120163
Kurtosis426.38013
Mean167.44186
Median Absolute Deviation (MAD)0
Skewness20.239993
Sum79200
Variance4118556.4
MonotonicityNot monotonic
2023-12-13T07:31:04.148565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 244
51.6%
1 27
 
5.7%
3 19
 
4.0%
2 19
 
4.0%
6 12
 
2.5%
4 10
 
2.1%
11 7
 
1.5%
10 6
 
1.3%
7 5
 
1.1%
5 5
 
1.1%
Other values (87) 119
25.2%
ValueCountFrequency (%)
0 244
51.6%
1 27
 
5.7%
2 19
 
4.0%
3 19
 
4.0%
4 10
 
2.1%
5 5
 
1.1%
6 12
 
2.5%
7 5
 
1.1%
8 3
 
0.6%
9 4
 
0.8%
ValueCountFrequency (%)
43082 1
0.2%
6732 1
0.2%
5332 1
0.2%
3869 1
0.2%
1670 1
0.2%
1497 1
0.2%
1013 1
0.2%
896 2
0.4%
713 1
0.2%
677 1
0.2%

법인체
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.811839
Minimum0
Maximum1539
Zeros325
Zeros (%)68.7%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-13T07:31:04.277512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile64.4
Maximum1539
Range1539
Interquartile range (IQR)2

Descriptive statistics

Standard deviation117.1431
Coefficient of variation (CV)5.3706198
Kurtosis88.938432
Mean21.811839
Median Absolute Deviation (MAD)0
Skewness8.781335
Sum10317
Variance13722.505
MonotonicityNot monotonic
2023-12-13T07:31:04.418684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 325
68.7%
1 20
 
4.2%
2 15
 
3.2%
4 9
 
1.9%
3 8
 
1.7%
8 6
 
1.3%
12 5
 
1.1%
6 5
 
1.1%
9 5
 
1.1%
10 4
 
0.8%
Other values (53) 71
 
15.0%
ValueCountFrequency (%)
0 325
68.7%
1 20
 
4.2%
2 15
 
3.2%
3 8
 
1.7%
4 9
 
1.9%
5 2
 
0.4%
6 5
 
1.1%
7 3
 
0.6%
8 6
 
1.3%
9 5
 
1.1%
ValueCountFrequency (%)
1539 1
0.2%
1047 1
0.2%
1004 1
0.2%
849 1
0.2%
622 1
0.2%
499 1
0.2%
471 1
0.2%
420 1
0.2%
362 1
0.2%
277 1
0.2%

Interactions

2023-12-13T07:31:00.441894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:57.118655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:57.710027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:58.412170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:59.113859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:59.824110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:31:00.558770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:57.212992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:57.831499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:58.512075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:59.206330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:59.916273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:31:00.671181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:57.314089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:57.945996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:58.619464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:59.333001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:31:00.036753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:31:00.780087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:57.409123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:58.059910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:58.735288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:59.449541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:31:00.146492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:31:00.885633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:57.507103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:58.176641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:58.871235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:59.582192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:31:00.242044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:31:00.996761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:57.594152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:58.293175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:58.989516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:59.696459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:31:00.339816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:31:04.544122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
범죄대분류범죄중분류발생검거검거인원(남)검거인원(여)불상법인체
범죄대분류1.0000.9400.1070.2660.2770.2610.0000.317
범죄중분류0.9401.0000.6720.4790.4790.1730.2410.000
발생0.1070.6721.0000.9020.8870.9500.9560.483
검거0.2660.4790.9021.0000.8860.8160.7360.496
검거인원(남)0.2770.4790.8870.8861.0000.9460.6260.362
검거인원(여)0.2610.1730.9500.8160.9461.0000.8680.340
불상0.0000.2410.9560.7360.6260.8681.0000.464
법인체0.3170.0000.4830.4960.3620.3400.4641.000
2023-12-13T07:31:04.663709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
범죄중분류범죄대분류
범죄중분류1.0000.552
범죄대분류0.5521.000
2023-12-13T07:31:04.748885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생검거검거인원(남)검거인원(여)불상법인체범죄대분류범죄중분류
발생1.0000.9960.9790.9000.7720.5470.0490.332
검거0.9961.0000.9790.8980.7490.5410.1150.221
검거인원(남)0.9790.9791.0000.9030.7840.5260.1280.217
검거인원(여)0.9000.8980.9031.0000.7620.5480.1230.068
불상0.7720.7490.7840.7621.0000.5420.0000.110
법인체0.5470.5410.5260.5480.5421.0000.1400.000
범죄대분류0.0490.1150.1280.1230.0000.1401.0000.552
범죄중분류0.3320.2210.2170.0680.1100.0000.5521.000

Missing values

2023-12-13T07:31:01.171840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:31:01.398087image/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

범죄대분류범죄중분류범죄소분류발생검거검거인원(남)검거인원(여)불상법인체
0강력범죄살인기수살인2041961875441
1강력범죄살인기수영아살해550500
2강력범죄살인기수존속살해262724300
3강력범죄살인기수촉탁·승낙살인215400
4강력범죄살인기수자살교사·방조2524121400
5강력범죄살인기수위계·위력·촉탁·승낙살인000000
6강력범죄살인기수특가법(보복살인등)334000
7강력범죄살인기수아동학대처벌법(아동학대살해)552300
8강력범죄살인미수등살인34333630846100
9강력범죄살인미수등영아살해332200
범죄대분류범죄중분류범죄소분류발생검거검거인원(남)검거인원(여)불상법인체
463기타범죄계폭력행위등처벌에관한법률위반(기타)8821800
464기타범죄계하수도법60574721218
465기타범죄계하천법1781177418527312
466기타범죄계학원의설립·운영및과외교섭에관한법률38037010628200
467기타범죄계항만운송사업법32293228581020
468기타범죄계허위감정,증거인멸·은닉,증인은닉죄995910347380
469기타범죄계형사소송법221300
470기타범죄계형의실효등에관한법률8811210
471기타범죄계화물자동차운수사업법29962978128564250
472기타범죄계기타30119288791394948805411539