Overview

Dataset statistics

Number of variables13
Number of observations183
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.9 KiB
Average record size in memory116.7 B

Variable types

Text1
Numeric12

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 전주지방검찰청이 관할하는 범죄 발생의 검거상황과 관련된 통계임
Author대검찰청
URLhttps://www.data.go.kr/data/15084776/fileData.do

Alerts

직수_인지_발생건수(건) is highly overall correlated with 직수_인지_검거건수(건) and 8 other fieldsHigh correlation
직수_인지_검거건수(건) is highly overall correlated with 직수_인지_발생건수(건) and 8 other fieldsHigh correlation
직수_인지_남자검거인원(명) is highly overall correlated with 직수_인지_발생건수(건) and 8 other fieldsHigh correlation
직수_인지_여자검거인원(명) is highly overall correlated with 직수_인지_발생건수(건) and 2 other fieldsHigh correlation
직수_인지_미상검거인원(명) is highly overall correlated with 직수_인지_발생건수(건) and 3 other fieldsHigh correlation
직수_인지_법인(개) is highly overall correlated with 지휘관할_법인(개)High correlation
지휘관할_발생건수(건) is highly overall correlated with 직수_인지_발생건수(건) and 5 other fieldsHigh correlation
지휘관할_검거건수(건) is highly overall correlated with 직수_인지_발생건수(건) and 5 other fieldsHigh correlation
지휘관할_남자검거인원(명) is highly overall correlated with 직수_인지_발생건수(건) and 6 other fieldsHigh correlation
지휘관할_여자검거인원(명) is highly overall correlated with 직수_인지_발생건수(건) and 6 other fieldsHigh correlation
지휘관할_미상검거인원(명) is highly overall correlated with 직수_인지_발생건수(건) and 5 other fieldsHigh correlation
지휘관할_법인(개) is highly overall correlated with 직수_인지_법인(개)High correlation
범죄분류 has unique valuesUnique
직수_인지_발생건수(건) has 93 (50.8%) zerosZeros
직수_인지_검거건수(건) has 100 (54.6%) zerosZeros
직수_인지_남자검거인원(명) has 97 (53.0%) zerosZeros
직수_인지_여자검거인원(명) has 138 (75.4%) zerosZeros
직수_인지_미상검거인원(명) has 157 (85.8%) zerosZeros
직수_인지_법인(개) has 159 (86.9%) zerosZeros
지휘관할_남자검거인원(명) has 3 (1.6%) zerosZeros
지휘관할_여자검거인원(명) has 42 (23.0%) zerosZeros
지휘관할_미상검거인원(명) has 123 (67.2%) zerosZeros
지휘관할_법인(개) has 112 (61.2%) zerosZeros

Reproduction

Analysis started2023-12-12 17:03:22.050650
Analysis finished2023-12-12 17:03:40.280102
Duration18.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct183
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-13T02:03:40.499587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length23
Mean length8.2185792
Min length2

Characters and Unicode

Total characters1504
Distinct characters240
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique183 ?
Unique (%)100.0%

Sample

1st row절도
2nd row불법사용
3rd row침입절도
4th row장물
5th row사기
ValueCountFrequency (%)
관한법률 25
 
8.6%
18
 
6.2%
마약류관리에 3
 
1.0%
관리에 3
 
1.0%
규제 2
 
0.7%
규제에 2
 
0.7%
처벌등에 2
 
0.7%
이용에 2
 
0.7%
안전관리에 2
 
0.7%
아동·청소년의 2
 
0.7%
Other values (229) 230
79.0%
2023-12-13T02:03:40.928373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
111
 
7.4%
108
 
7.2%
61
 
4.1%
35
 
2.3%
34
 
2.3%
30
 
2.0%
29
 
1.9%
26
 
1.7%
24
 
1.6%
22
 
1.5%
Other values (230) 1024
68.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1348
89.6%
Space Separator 108
 
7.2%
Other Punctuation 18
 
1.2%
Close Punctuation 15
 
1.0%
Open Punctuation 15
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
111
 
8.2%
61
 
4.5%
35
 
2.6%
34
 
2.5%
30
 
2.2%
29
 
2.2%
26
 
1.9%
24
 
1.8%
22
 
1.6%
22
 
1.6%
Other values (224) 954
70.8%
Other Punctuation
ValueCountFrequency (%)
, 10
55.6%
· 7
38.9%
/ 1
 
5.6%
Space Separator
ValueCountFrequency (%)
108
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1348
89.6%
Common 156
 
10.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
111
 
8.2%
61
 
4.5%
35
 
2.6%
34
 
2.5%
30
 
2.2%
29
 
2.2%
26
 
1.9%
24
 
1.8%
22
 
1.6%
22
 
1.6%
Other values (224) 954
70.8%
Common
ValueCountFrequency (%)
108
69.2%
) 15
 
9.6%
( 15
 
9.6%
, 10
 
6.4%
· 7
 
4.5%
/ 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1348
89.6%
ASCII 149
 
9.9%
None 7
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
111
 
8.2%
61
 
4.5%
35
 
2.6%
34
 
2.5%
30
 
2.2%
29
 
2.2%
26
 
1.9%
24
 
1.8%
22
 
1.6%
22
 
1.6%
Other values (224) 954
70.8%
ASCII
ValueCountFrequency (%)
108
72.5%
) 15
 
10.1%
( 15
 
10.1%
, 10
 
6.7%
/ 1
 
0.7%
None
ValueCountFrequency (%)
· 7
100.0%

직수_인지_발생건수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0273224
Minimum0
Maximum181
Zeros93
Zeros (%)50.8%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T02:03:41.078197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile21.6
Maximum181
Range181
Interquartile range (IQR)5

Descriptive statistics

Standard deviation18.073547
Coefficient of variation (CV)2.998603
Kurtosis53.392506
Mean6.0273224
Median Absolute Deviation (MAD)0
Skewness6.55186
Sum1103
Variance326.6531
MonotonicityNot monotonic
2023-12-13T02:03:41.240421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 93
50.8%
1 23
 
12.6%
2 11
 
6.0%
11 6
 
3.3%
7 5
 
2.7%
5 5
 
2.7%
3 4
 
2.2%
4 4
 
2.2%
17 4
 
2.2%
13 4
 
2.2%
Other values (18) 24
 
13.1%
ValueCountFrequency (%)
0 93
50.8%
1 23
 
12.6%
2 11
 
6.0%
3 4
 
2.2%
4 4
 
2.2%
5 5
 
2.7%
6 3
 
1.6%
7 5
 
2.7%
8 2
 
1.1%
9 1
 
0.5%
ValueCountFrequency (%)
181 1
0.5%
95 1
0.5%
85 1
0.5%
67 1
0.5%
53 1
0.5%
42 1
0.5%
34 1
0.5%
29 1
0.5%
24 1
0.5%
22 1
0.5%

직수_인지_검거건수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6939891
Minimum0
Maximum118
Zeros100
Zeros (%)54.6%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T02:03:41.372398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile18.7
Maximum118
Range118
Interquartile range (IQR)4

Descriptive statistics

Standard deviation13.086119
Coefficient of variation (CV)2.787846
Kurtosis38.782346
Mean4.6939891
Median Absolute Deviation (MAD)0
Skewness5.6276419
Sum859
Variance171.2465
MonotonicityNot monotonic
2023-12-13T02:03:41.491361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 100
54.6%
1 18
 
9.8%
2 11
 
6.0%
3 7
 
3.8%
7 6
 
3.3%
4 6
 
3.3%
13 4
 
2.2%
8 4
 
2.2%
10 3
 
1.6%
6 3
 
1.6%
Other values (16) 21
 
11.5%
ValueCountFrequency (%)
0 100
54.6%
1 18
 
9.8%
2 11
 
6.0%
3 7
 
3.8%
4 6
 
3.3%
5 2
 
1.1%
6 3
 
1.6%
7 6
 
3.3%
8 4
 
2.2%
9 2
 
1.1%
ValueCountFrequency (%)
118 1
0.5%
82 1
0.5%
54 1
0.5%
50 1
0.5%
46 1
0.5%
42 1
0.5%
26 1
0.5%
22 1
0.5%
20 1
0.5%
19 1
0.5%

직수_인지_남자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6338798
Minimum0
Maximum153
Zeros97
Zeros (%)53.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T02:03:41.609406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile37.4
Maximum153
Range153
Interquartile range (IQR)4

Descriptive statistics

Standard deviation18.963016
Coefficient of variation (CV)2.8585107
Kurtosis29.607741
Mean6.6338798
Median Absolute Deviation (MAD)0
Skewness5.0445744
Sum1214
Variance359.59599
MonotonicityNot monotonic
2023-12-13T02:03:41.736629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 97
53.0%
1 22
 
12.0%
3 8
 
4.4%
2 8
 
4.4%
4 5
 
2.7%
10 5
 
2.7%
8 3
 
1.6%
14 3
 
1.6%
6 3
 
1.6%
7 3
 
1.6%
Other values (21) 26
 
14.2%
ValueCountFrequency (%)
0 97
53.0%
1 22
 
12.0%
2 8
 
4.4%
3 8
 
4.4%
4 5
 
2.7%
5 2
 
1.1%
6 3
 
1.6%
7 3
 
1.6%
8 3
 
1.6%
9 3
 
1.6%
ValueCountFrequency (%)
153 1
0.5%
114 1
0.5%
107 1
0.5%
84 1
0.5%
57 1
0.5%
42 2
1.1%
41 1
0.5%
40 1
0.5%
38 1
0.5%
32 1
0.5%

직수_인지_여자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2404372
Minimum0
Maximum31
Zeros138
Zeros (%)75.4%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T02:03:41.884092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum31
Range31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.9872191
Coefficient of variation (CV)3.2143661
Kurtosis27.816226
Mean1.2404372
Median Absolute Deviation (MAD)0
Skewness4.998537
Sum227
Variance15.897916
MonotonicityNot monotonic
2023-12-13T02:03:41.994791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 138
75.4%
1 15
 
8.2%
2 10
 
5.5%
3 3
 
1.6%
4 3
 
1.6%
5 3
 
1.6%
7 2
 
1.1%
6 2
 
1.1%
31 1
 
0.5%
8 1
 
0.5%
Other values (5) 5
 
2.7%
ValueCountFrequency (%)
0 138
75.4%
1 15
 
8.2%
2 10
 
5.5%
3 3
 
1.6%
4 3
 
1.6%
5 3
 
1.6%
6 2
 
1.1%
7 2
 
1.1%
8 1
 
0.5%
9 1
 
0.5%
ValueCountFrequency (%)
31 1
 
0.5%
23 1
 
0.5%
21 1
 
0.5%
20 1
 
0.5%
18 1
 
0.5%
9 1
 
0.5%
8 1
 
0.5%
7 2
1.1%
6 2
1.1%
5 3
1.6%

직수_인지_미상검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6557377
Minimum0
Maximum18
Zeros157
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T02:03:42.091553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.9
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.3896658
Coefficient of variation (CV)3.6442403
Kurtosis31.792022
Mean0.6557377
Median Absolute Deviation (MAD)0
Skewness5.2603477
Sum120
Variance5.7105026
MonotonicityNot monotonic
2023-12-13T02:03:42.198440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 157
85.8%
1 8
 
4.4%
3 6
 
3.3%
9 3
 
1.6%
4 2
 
1.1%
18 2
 
1.1%
2 2
 
1.1%
5 1
 
0.5%
8 1
 
0.5%
6 1
 
0.5%
ValueCountFrequency (%)
0 157
85.8%
1 8
 
4.4%
2 2
 
1.1%
3 6
 
3.3%
4 2
 
1.1%
5 1
 
0.5%
6 1
 
0.5%
8 1
 
0.5%
9 3
 
1.6%
18 2
 
1.1%
ValueCountFrequency (%)
18 2
 
1.1%
9 3
 
1.6%
8 1
 
0.5%
6 1
 
0.5%
5 1
 
0.5%
4 2
 
1.1%
3 6
 
3.3%
2 2
 
1.1%
1 8
 
4.4%
0 157
85.8%

직수_인지_법인(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61748634
Minimum0
Maximum34
Zeros159
Zeros (%)86.9%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T02:03:42.304970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum34
Range34
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.4380099
Coefficient of variation (CV)5.5677506
Kurtosis77.860618
Mean0.61748634
Median Absolute Deviation (MAD)0
Skewness8.6233765
Sum113
Variance11.819912
MonotonicityNot monotonic
2023-12-13T02:03:42.402181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 159
86.9%
1 9
 
4.9%
2 6
 
3.3%
3 4
 
2.2%
4 2
 
1.1%
8 1
 
0.5%
30 1
 
0.5%
34 1
 
0.5%
ValueCountFrequency (%)
0 159
86.9%
1 9
 
4.9%
2 6
 
3.3%
3 4
 
2.2%
4 2
 
1.1%
8 1
 
0.5%
30 1
 
0.5%
34 1
 
0.5%
ValueCountFrequency (%)
34 1
 
0.5%
30 1
 
0.5%
8 1
 
0.5%
4 2
 
1.1%
3 4
 
2.2%
2 6
 
3.3%
1 9
 
4.9%
0 159
86.9%

지휘관할_발생건수(건)
Real number (ℝ)

HIGH CORRELATION 

Distinct114
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308.40437
Minimum0
Maximum8048
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T02:03:42.551624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1
Q18
median34
Q3171
95-th percentile1197
Maximum8048
Range8048
Interquartile range (IQR)163

Descriptive statistics

Standard deviation1030.3816
Coefficient of variation (CV)3.3410084
Kurtosis31.959779
Mean308.40437
Median Absolute Deviation (MAD)31
Skewness5.4974041
Sum56438
Variance1061686.2
MonotonicityNot monotonic
2023-12-13T02:03:43.129803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 12
 
6.6%
1 9
 
4.9%
5 8
 
4.4%
6 6
 
3.3%
8 5
 
2.7%
3 5
 
2.7%
12 4
 
2.2%
4 4
 
2.2%
18 3
 
1.6%
10 3
 
1.6%
Other values (104) 124
67.8%
ValueCountFrequency (%)
0 1
 
0.5%
1 9
4.9%
2 12
6.6%
3 5
2.7%
4 4
 
2.2%
5 8
4.4%
6 6
3.3%
8 5
2.7%
9 1
 
0.5%
10 3
 
1.6%
ValueCountFrequency (%)
8048 1
0.5%
6377 1
0.5%
5929 1
0.5%
5122 1
0.5%
4929 1
0.5%
1759 1
0.5%
1703 1
0.5%
1543 1
0.5%
1361 1
0.5%
1210 1
0.5%

지휘관할_검거건수(건)
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)57.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean280.60656
Minimum1
Maximum7101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T02:03:43.299875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16.5
median30
Q3160.5
95-th percentile972
Maximum7101
Range7100
Interquartile range (IQR)154

Descriptive statistics

Standard deviation941.90627
Coefficient of variation (CV)3.3566795
Kurtosis32.818187
Mean280.60656
Median Absolute Deviation (MAD)27
Skewness5.5869216
Sum51351
Variance887187.43
MonotonicityNot monotonic
2023-12-13T02:03:43.483131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 15
 
8.2%
3 9
 
4.9%
2 7
 
3.8%
5 6
 
3.3%
16 5
 
2.7%
6 5
 
2.7%
8 5
 
2.7%
13 5
 
2.7%
4 4
 
2.2%
21 4
 
2.2%
Other values (96) 118
64.5%
ValueCountFrequency (%)
1 15
8.2%
2 7
3.8%
3 9
4.9%
4 4
 
2.2%
5 6
 
3.3%
6 5
 
2.7%
7 1
 
0.5%
8 5
 
2.7%
9 1
 
0.5%
10 2
 
1.1%
ValueCountFrequency (%)
7101 1
0.5%
6290 1
0.5%
5845 1
0.5%
4802 1
0.5%
3563 1
0.5%
1731 1
0.5%
1688 1
0.5%
1283 1
0.5%
1030 1
0.5%
983 1
0.5%

지휘관할_남자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct109
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260.01639
Minimum0
Maximum5844
Zeros3
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T02:03:43.651639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median31
Q3119
95-th percentile892
Maximum5844
Range5844
Interquartile range (IQR)112

Descriptive statistics

Standard deviation836.12819
Coefficient of variation (CV)3.2156749
Kurtosis30.411856
Mean260.01639
Median Absolute Deviation (MAD)28
Skewness5.3881387
Sum47583
Variance699110.36
MonotonicityNot monotonic
2023-12-13T02:03:43.784478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10
 
5.5%
2 9
 
4.9%
4 8
 
4.4%
3 7
 
3.8%
11 6
 
3.3%
7 5
 
2.7%
29 4
 
2.2%
15 3
 
1.6%
0 3
 
1.6%
21 3
 
1.6%
Other values (99) 125
68.3%
ValueCountFrequency (%)
0 3
 
1.6%
1 10
5.5%
2 9
4.9%
3 7
3.8%
4 8
4.4%
5 3
 
1.6%
6 2
 
1.1%
7 5
2.7%
8 2
 
1.1%
9 3
 
1.6%
ValueCountFrequency (%)
5844 1
0.5%
5418 1
0.5%
5272 1
0.5%
5059 1
0.5%
2302 1
0.5%
2295 1
0.5%
1431 1
0.5%
1188 1
0.5%
993 1
0.5%
893 1
0.5%

지휘관할_여자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.043716
Minimum0
Maximum1645
Zeros42
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T02:03:43.920113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q323.5
95-th percentile220.7
Maximum1645
Range1645
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation208.95943
Coefficient of variation (CV)3.4231112
Kurtosis38.294901
Mean61.043716
Median Absolute Deviation (MAD)5
Skewness5.9317853
Sum11171
Variance43664.042
MonotonicityNot monotonic
2023-12-13T02:03:44.076611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42
23.0%
1 20
 
10.9%
3 9
 
4.9%
5 9
 
4.9%
2 9
 
4.9%
4 6
 
3.3%
9 5
 
2.7%
6 5
 
2.7%
10 4
 
2.2%
8 4
 
2.2%
Other values (54) 70
38.3%
ValueCountFrequency (%)
0 42
23.0%
1 20
10.9%
2 9
 
4.9%
3 9
 
4.9%
4 6
 
3.3%
5 9
 
4.9%
6 5
 
2.7%
7 4
 
2.2%
8 4
 
2.2%
9 5
 
2.7%
ValueCountFrequency (%)
1645 1
0.5%
1539 1
0.5%
1320 1
0.5%
673 1
0.5%
635 1
0.5%
474 1
0.5%
425 1
0.5%
299 1
0.5%
230 1
0.5%
221 1
0.5%

지휘관할_미상검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9071038
Minimum0
Maximum77
Zeros123
Zeros (%)67.2%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T02:03:44.205301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile23.5
Maximum77
Range77
Interquartile range (IQR)1

Descriptive statistics

Standard deviation8.9385634
Coefficient of variation (CV)3.0747314
Kurtosis31.027994
Mean2.9071038
Median Absolute Deviation (MAD)0
Skewness5.0245126
Sum532
Variance79.897916
MonotonicityNot monotonic
2023-12-13T02:03:44.350611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 123
67.2%
1 20
 
10.9%
3 8
 
4.4%
2 4
 
2.2%
5 4
 
2.2%
9 3
 
1.6%
4 3
 
1.6%
32 3
 
1.6%
29 3
 
1.6%
7 3
 
1.6%
Other values (6) 9
 
4.9%
ValueCountFrequency (%)
0 123
67.2%
1 20
 
10.9%
2 4
 
2.2%
3 8
 
4.4%
4 3
 
1.6%
5 4
 
2.2%
6 1
 
0.5%
7 3
 
1.6%
8 2
 
1.1%
9 3
 
1.6%
ValueCountFrequency (%)
77 1
 
0.5%
48 1
 
0.5%
32 3
1.6%
29 3
1.6%
25 2
1.1%
10 2
1.1%
9 3
1.6%
8 2
1.1%
7 3
1.6%
6 1
 
0.5%

지휘관할_법인(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6775956
Minimum0
Maximum364
Zeros112
Zeros (%)61.2%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T02:03:44.497408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile18.9
Maximum364
Range364
Interquartile range (IQR)2

Descriptive statistics

Standard deviation29.568135
Coefficient of variation (CV)5.2078621
Kurtosis120.8942
Mean5.6775956
Median Absolute Deviation (MAD)0
Skewness10.341131
Sum1039
Variance874.27461
MonotonicityNot monotonic
2023-12-13T02:03:44.649993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 112
61.2%
1 24
 
13.1%
3 12
 
6.6%
2 10
 
5.5%
11 3
 
1.6%
4 3
 
1.6%
12 2
 
1.1%
9 2
 
1.1%
32 2
 
1.1%
364 1
 
0.5%
Other values (12) 12
 
6.6%
ValueCountFrequency (%)
0 112
61.2%
1 24
 
13.1%
2 10
 
5.5%
3 12
 
6.6%
4 3
 
1.6%
6 1
 
0.5%
7 1
 
0.5%
9 2
 
1.1%
11 3
 
1.6%
12 2
 
1.1%
ValueCountFrequency (%)
364 1
0.5%
115 1
0.5%
82 1
0.5%
70 1
0.5%
45 1
0.5%
32 2
1.1%
29 1
0.5%
21 1
0.5%
19 1
0.5%
18 1
0.5%

Interactions

2023-12-13T02:03:38.534564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:22.613584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:24.433868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:25.731882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:27.302103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:28.423566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:29.809542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:31.186429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:32.638708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:33.899689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:35.371271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:37.179781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:38.640999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:22.743506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:24.533245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:25.861946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:27.400301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:28.513262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:29.899435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:31.334386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:32.739200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:34.047997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:35.468157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:37.293881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:38.738271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:22.853511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:24.619256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:25.982120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:27.509607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:28.596067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:29.992682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:31.439196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:32.821956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:34.176666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:35.573551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:37.404178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:38.841957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:22.998948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:24.712404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:26.129807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:27.618157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:28.688920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:30.086576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:31.572402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:32.937190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:34.301243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:35.692212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:37.510645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:38.931458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:23.109448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:24.818660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:26.279041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:27.706695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:28.775023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:30.181873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:31.705689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:33.035248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:34.391358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:35.793612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:37.665920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:39.049120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:23.568088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:24.945495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:26.423064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:27.811426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:28.854505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:30.309740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:31.837470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:33.143622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:34.490925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:35.902203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:37.783367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:39.178898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:23.670126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:25.069934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:26.561582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:27.890808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:28.943628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:30.409924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:31.947605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:33.262847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:34.593186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:36.043163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:37.893700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:39.309909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:23.784062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:25.196275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:26.705640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:27.987962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:29.047832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:30.559564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:32.071419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:33.352875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:34.737621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:36.175480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:38.001808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:39.408606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:23.895025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:25.301085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:26.827639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:28.072265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:29.134521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:30.661788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:32.170460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:33.446244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:34.862809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:36.688809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:38.113110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:39.514621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:24.027657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:25.398312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:26.938613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:28.170657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:29.221619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:30.778560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:32.294725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:33.535138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:34.990499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:36.817028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:38.217773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:39.634253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:24.132109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:25.495263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:27.039644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:28.256042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:29.327554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:30.899411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:32.394698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:33.655149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:35.100004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:36.930653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:38.325089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:39.779394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:24.284961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:25.611063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:27.176493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:28.341761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:29.440570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:31.046837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:32.524468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:33.771837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:35.275694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:37.062409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:03:38.420851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:03:44.826240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
직수_인지_발생건수(건)직수_인지_검거건수(건)직수_인지_남자검거인원(명)직수_인지_여자검거인원(명)직수_인지_미상검거인원(명)직수_인지_법인(개)지휘관할_발생건수(건)지휘관할_검거건수(건)지휘관할_남자검거인원(명)지휘관할_여자검거인원(명)지휘관할_미상검거인원(명)지휘관할_법인(개)
직수_인지_발생건수(건)1.0000.9910.8350.9640.8540.5050.6660.8310.4460.7610.7150.445
직수_인지_검거건수(건)0.9911.0000.8560.9660.8070.6320.7360.8840.5870.8550.7170.690
직수_인지_남자검거인원(명)0.8350.8561.0000.8990.6880.3770.7630.7500.6880.6070.7500.386
직수_인지_여자검거인원(명)0.9640.9660.8991.0000.8230.0000.6530.8240.5060.6900.7050.000
직수_인지_미상검거인원(명)0.8540.8070.6880.8231.0000.1710.6030.7790.5870.7320.6770.000
직수_인지_법인(개)0.5050.6320.3770.0000.1711.0000.2720.2640.2720.2640.0000.954
지휘관할_발생건수(건)0.6660.7360.7630.6530.6030.2721.0000.9850.9600.9070.9030.439
지휘관할_검거건수(건)0.8310.8840.7500.8240.7790.2640.9851.0000.8730.9910.7650.449
지휘관할_남자검거인원(명)0.4460.5870.6880.5060.5870.2720.9600.8731.0000.8390.8610.440
지휘관할_여자검거인원(명)0.7610.8550.6070.6900.7320.2640.9070.9910.8391.0000.6830.449
지휘관할_미상검거인원(명)0.7150.7170.7500.7050.6770.0000.9030.7650.8610.6831.0000.220
지휘관할_법인(개)0.4450.6900.3860.0000.0000.9540.4390.4490.4400.4490.2201.000
2023-12-13T02:03:45.042219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
직수_인지_발생건수(건)직수_인지_검거건수(건)직수_인지_남자검거인원(명)직수_인지_여자검거인원(명)직수_인지_미상검거인원(명)직수_인지_법인(개)지휘관할_발생건수(건)지휘관할_검거건수(건)지휘관할_남자검거인원(명)지휘관할_여자검거인원(명)지휘관할_미상검거인원(명)지휘관할_법인(개)
직수_인지_발생건수(건)1.0000.9590.8600.6130.5570.2530.5450.5400.5970.5240.5420.167
직수_인지_검거건수(건)0.9591.0000.8590.6270.5190.2770.5310.5280.5790.5230.5150.208
직수_인지_남자검거인원(명)0.8600.8591.0000.6000.5010.3740.6050.6060.6590.5060.5290.296
직수_인지_여자검거인원(명)0.6130.6270.6001.0000.4110.3260.3960.4010.4300.4930.4180.264
직수_인지_미상검거인원(명)0.5570.5190.5010.4111.0000.2230.3200.3130.3280.3170.6610.094
직수_인지_법인(개)0.2530.2770.3740.3260.2231.0000.2960.2980.2850.2460.3390.584
지휘관할_발생건수(건)0.5450.5310.6050.3960.3200.2961.0000.9890.9430.8140.4870.284
지휘관할_검거건수(건)0.5400.5280.6060.4010.3130.2980.9891.0000.9520.8150.4750.302
지휘관할_남자검거인원(명)0.5970.5790.6590.4300.3280.2850.9430.9521.0000.7960.5160.274
지휘관할_여자검거인원(명)0.5240.5230.5060.4930.3170.2460.8140.8150.7961.0000.5120.248
지휘관할_미상검거인원(명)0.5420.5150.5290.4180.6610.3390.4870.4750.5160.5121.0000.319
지휘관할_법인(개)0.1670.2080.2960.2640.0940.5840.2840.3020.2740.2480.3191.000

Missing values

2023-12-13T02:03:39.940583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:03:40.161716image/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

범죄분류직수_인지_발생건수(건)직수_인지_검거건수(건)직수_인지_남자검거인원(명)직수_인지_여자검거인원(명)직수_인지_미상검거인원(명)직수_인지_법인(개)지휘관할_발생건수(건)지휘관할_검거건수(건)지휘관할_남자검거인원(명)지휘관할_여자검거인원(명)지휘관할_미상검거인원(명)지휘관할_법인(개)
0절도21100151223563230263581
1불법사용000000394147400
2침입절도000000286256122800
3장물00000019118026500
4사기181118153319380487101541816457729
5컴퓨터등사용사기2210001265647913
6부당이득000000334000
7편의시설부정이용000000472830410
8전기통신금융사기피해금환급에관한특별법0000004019171410
9보험사기방지특별법1010008185542000
범죄분류직수_인지_발생건수(건)직수_인지_검거건수(건)직수_인지_남자검거인원(명)직수_인지_여자검거인원(명)직수_인지_미상검거인원(명)직수_인지_법인(개)지휘관할_발생건수(건)지휘관할_검거건수(건)지휘관할_남자검거인원(명)지휘관할_여자검거인원(명)지휘관할_미상검거인원(명)지휘관할_법인(개)
173특가법(도주차량)0000002352172325110
174특허법000000220270
175폐기물관리법42424210341111101048270
176풍속영업의 규제에 관한법률000000551600
177하천법000000242422501
178학원의설립운영 및 과외교습에 관한법률000000212181300
179화물자동차 운수사업법000000323233402
180화재예방·소방시설설치유지 및 안전관리에 관한법률000000444003
181화학물질관리법000000566102
182기타특별법29262332417591731143147425115