Overview

Dataset statistics

Number of variables13
Number of observations190
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.7 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 창원지방검찰청이 관할하는 범죄 발생의 검거상황과 관련된 통계임
Author대검찰청
URLhttps://www.data.go.kr/data/15084773/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 7 other fieldsHigh correlation
직수_인지_여자검거인원(명) is highly overall correlated with 직수_인지_발생건수(건) and 4 other fieldsHigh correlation
직수_인지_미상검거인원(명) is highly overall correlated with 직수_인지_발생건수(건) and 4 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 6 other fieldsHigh correlation
지휘관할_검거건수(건) is highly overall correlated with 직수_인지_발생건수(건) and 6 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 7 other fieldsHigh correlation
지휘관할_미상검거인원(명) is highly overall correlated with 직수_인지_발생건수(건) and 6 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 76 (40.0%) zerosZeros
직수_인지_검거건수(건) has 75 (39.5%) zerosZeros
직수_인지_남자검거인원(명) has 89 (46.8%) zerosZeros
직수_인지_여자검거인원(명) has 137 (72.1%) zerosZeros
직수_인지_미상검거인원(명) has 153 (80.5%) zerosZeros
직수_인지_법인(개) has 165 (86.8%) zerosZeros
지휘관할_발생건수(건) has 2 (1.1%) zerosZeros
지휘관할_남자검거인원(명) has 3 (1.6%) zerosZeros
지휘관할_여자검거인원(명) has 31 (16.3%) zerosZeros
지휘관할_미상검거인원(명) has 112 (58.9%) zerosZeros
지휘관할_법인(개) has 113 (59.5%) zerosZeros

Reproduction

Analysis started2023-12-12 23:01:01.026348
Analysis finished2023-12-12 23:01:15.352573
Duration14.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct190
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-13T08:01:15.518293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length8.0842105
Min length2

Characters and Unicode

Total characters1536
Distinct characters243
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

Unique190 ?
Unique (%)100.0%

Sample

1st row절도
2nd row불법사용
3rd row침입절도
4th row장물
5th row사기
ValueCountFrequency (%)
관한법률 25
 
8.3%
20
 
6.6%
관리에 4
 
1.3%
마약류관리에 3
 
1.0%
아동·청소년의 2
 
0.7%
성보호에 2
 
0.7%
처벌등에 2
 
0.7%
규제 2
 
0.7%
안전관리에 2
 
0.7%
이용에 2
 
0.7%
Other values (236) 237
78.7%
2023-12-13T08:01:15.908801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
112
 
7.3%
111
 
7.2%
62
 
4.0%
35
 
2.3%
34
 
2.2%
30
 
2.0%
29
 
1.9%
26
 
1.7%
23
 
1.5%
23
 
1.5%
Other values (233) 1051
68.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1376
89.6%
Space Separator 111
 
7.2%
Other Punctuation 19
 
1.2%
Close Punctuation 15
 
1.0%
Open Punctuation 15
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
112
 
8.1%
62
 
4.5%
35
 
2.5%
34
 
2.5%
30
 
2.2%
29
 
2.1%
26
 
1.9%
23
 
1.7%
23
 
1.7%
22
 
1.6%
Other values (227) 980
71.2%
Other Punctuation
ValueCountFrequency (%)
, 10
52.6%
· 7
36.8%
/ 2
 
10.5%
Space Separator
ValueCountFrequency (%)
111
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1376
89.6%
Common 160
 
10.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
112
 
8.1%
62
 
4.5%
35
 
2.5%
34
 
2.5%
30
 
2.2%
29
 
2.1%
26
 
1.9%
23
 
1.7%
23
 
1.7%
22
 
1.6%
Other values (227) 980
71.2%
Common
ValueCountFrequency (%)
111
69.4%
) 15
 
9.4%
( 15
 
9.4%
, 10
 
6.2%
· 7
 
4.4%
/ 2
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1376
89.6%
ASCII 153
 
10.0%
None 7
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
112
 
8.1%
62
 
4.5%
35
 
2.5%
34
 
2.5%
30
 
2.2%
29
 
2.1%
26
 
1.9%
23
 
1.7%
23
 
1.7%
22
 
1.6%
Other values (227) 980
71.2%
ASCII
ValueCountFrequency (%)
111
72.5%
) 15
 
9.8%
( 15
 
9.8%
, 10
 
6.5%
/ 2
 
1.3%
None
ValueCountFrequency (%)
· 7
100.0%

직수_인지_발생건수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.494737
Minimum0
Maximum283
Zeros76
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T08:01:16.043679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37
95-th percentile45.5
Maximum283
Range283
Interquartile range (IQR)7

Descriptive statistics

Standard deviation30.806058
Coefficient of variation (CV)2.9353816
Kurtosis43.490379
Mean10.494737
Median Absolute Deviation (MAD)1
Skewness6.0043324
Sum1994
Variance949.0132
MonotonicityNot monotonic
2023-12-13T08:01:16.185683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 76
40.0%
1 24
 
12.6%
2 16
 
8.4%
5 10
 
5.3%
3 5
 
2.6%
6 5
 
2.6%
4 5
 
2.6%
15 4
 
2.1%
8 4
 
2.1%
7 3
 
1.6%
Other values (30) 38
20.0%
ValueCountFrequency (%)
0 76
40.0%
1 24
 
12.6%
2 16
 
8.4%
3 5
 
2.6%
4 5
 
2.6%
5 10
 
5.3%
6 5
 
2.6%
7 3
 
1.6%
8 4
 
2.1%
9 2
 
1.1%
ValueCountFrequency (%)
283 1
0.5%
217 1
0.5%
115 1
0.5%
109 1
0.5%
91 1
0.5%
90 1
0.5%
81 1
0.5%
69 1
0.5%
54 1
0.5%
50 1
0.5%

직수_인지_검거건수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3578947
Minimum0
Maximum239
Zeros75
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T08:01:16.343903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37
95-th percentile36.85
Maximum239
Range239
Interquartile range (IQR)7

Descriptive statistics

Standard deviation26.845005
Coefficient of variation (CV)2.8687013
Kurtosis39.705455
Mean9.3578947
Median Absolute Deviation (MAD)1
Skewness5.7682404
Sum1778
Variance720.6543
MonotonicityNot monotonic
2023-12-13T08:01:16.487496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 75
39.5%
1 27
 
14.2%
2 16
 
8.4%
5 11
 
5.8%
7 8
 
4.2%
3 5
 
2.6%
4 4
 
2.1%
14 3
 
1.6%
11 3
 
1.6%
9 2
 
1.1%
Other values (27) 36
18.9%
ValueCountFrequency (%)
0 75
39.5%
1 27
 
14.2%
2 16
 
8.4%
3 5
 
2.6%
4 4
 
2.1%
5 11
 
5.8%
6 2
 
1.1%
7 8
 
4.2%
8 2
 
1.1%
9 2
 
1.1%
ValueCountFrequency (%)
239 1
0.5%
188 1
0.5%
109 1
0.5%
104 1
0.5%
90 1
0.5%
75 1
0.5%
74 1
0.5%
53 1
0.5%
49 1
0.5%
40 1
0.5%

직수_인지_남자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4736842
Minimum0
Maximum273
Zeros89
Zeros (%)46.8%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T08:01:16.886350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile29
Maximum273
Range273
Interquartile range (IQR)5

Descriptive statistics

Standard deviation24.575482
Coefficient of variation (CV)3.2882688
Kurtosis74.682851
Mean7.4736842
Median Absolute Deviation (MAD)1
Skewness7.6946026
Sum1420
Variance603.95433
MonotonicityNot monotonic
2023-12-13T08:01:17.020252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 89
46.8%
1 23
 
12.1%
3 13
 
6.8%
2 10
 
5.3%
5 9
 
4.7%
6 5
 
2.6%
12 5
 
2.6%
4 4
 
2.1%
9 4
 
2.1%
13 3
 
1.6%
Other values (21) 25
 
13.2%
ValueCountFrequency (%)
0 89
46.8%
1 23
 
12.1%
2 10
 
5.3%
3 13
 
6.8%
4 4
 
2.1%
5 9
 
4.7%
6 5
 
2.6%
7 1
 
0.5%
9 4
 
2.1%
10 2
 
1.1%
ValueCountFrequency (%)
273 1
0.5%
112 1
0.5%
87 1
0.5%
75 1
0.5%
74 1
0.5%
64 1
0.5%
57 1
0.5%
46 1
0.5%
41 1
0.5%
29 2
1.1%

직수_인지_여자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3105263
Minimum0
Maximum52
Zeros137
Zeros (%)72.1%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T08:01:17.134690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum52
Range52
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.1273099
Coefficient of variation (CV)3.9124051
Kurtosis59.342136
Mean1.3105263
Median Absolute Deviation (MAD)0
Skewness7.1095722
Sum249
Variance26.289307
MonotonicityNot monotonic
2023-12-13T08:01:17.256181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 137
72.1%
1 25
 
13.2%
2 9
 
4.7%
3 7
 
3.7%
4 3
 
1.6%
52 1
 
0.5%
16 1
 
0.5%
18 1
 
0.5%
8 1
 
0.5%
15 1
 
0.5%
Other values (4) 4
 
2.1%
ValueCountFrequency (%)
0 137
72.1%
1 25
 
13.2%
2 9
 
4.7%
3 7
 
3.7%
4 3
 
1.6%
5 1
 
0.5%
6 1
 
0.5%
8 1
 
0.5%
15 1
 
0.5%
16 1
 
0.5%
ValueCountFrequency (%)
52 1
 
0.5%
33 1
 
0.5%
20 1
 
0.5%
18 1
 
0.5%
16 1
 
0.5%
15 1
 
0.5%
8 1
 
0.5%
6 1
 
0.5%
5 1
 
0.5%
4 3
1.6%

직수_인지_미상검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1842105
Minimum0
Maximum108
Zeros153
Zeros (%)80.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T08:01:17.375496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9
Maximum108
Range108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.064489
Coefficient of variation (CV)4.6078381
Kurtosis71.923591
Mean2.1842105
Median Absolute Deviation (MAD)0
Skewness7.8315914
Sum415
Variance101.29393
MonotonicityNot monotonic
2023-12-13T08:01:17.511639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 153
80.5%
1 12
 
6.3%
6 5
 
2.6%
2 5
 
2.6%
9 2
 
1.1%
4 2
 
1.1%
20 1
 
0.5%
5 1
 
0.5%
12 1
 
0.5%
8 1
 
0.5%
Other values (7) 7
 
3.7%
ValueCountFrequency (%)
0 153
80.5%
1 12
 
6.3%
2 5
 
2.6%
4 2
 
1.1%
5 1
 
0.5%
6 5
 
2.6%
8 1
 
0.5%
9 2
 
1.1%
11 1
 
0.5%
12 1
 
0.5%
ValueCountFrequency (%)
108 1
0.5%
61 1
0.5%
42 1
0.5%
29 1
0.5%
26 1
0.5%
20 1
0.5%
15 1
0.5%
12 1
0.5%
11 1
0.5%
9 2
1.1%

직수_인지_법인(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5
Minimum0
Maximum16
Zeros165
Zeros (%)86.8%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T08:01:17.613496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.55
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1200729
Coefficient of variation (CV)4.2401457
Kurtosis33.046674
Mean0.5
Median Absolute Deviation (MAD)0
Skewness5.6311741
Sum95
Variance4.494709
MonotonicityNot monotonic
2023-12-13T08:01:17.730481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 165
86.8%
1 15
 
7.9%
13 3
 
1.6%
4 2
 
1.1%
5 1
 
0.5%
2 1
 
0.5%
3 1
 
0.5%
16 1
 
0.5%
7 1
 
0.5%
ValueCountFrequency (%)
0 165
86.8%
1 15
 
7.9%
2 1
 
0.5%
3 1
 
0.5%
4 2
 
1.1%
5 1
 
0.5%
7 1
 
0.5%
13 3
 
1.6%
16 1
 
0.5%
ValueCountFrequency (%)
16 1
 
0.5%
13 3
 
1.6%
7 1
 
0.5%
5 1
 
0.5%
4 2
 
1.1%
3 1
 
0.5%
2 1
 
0.5%
1 15
 
7.9%
0 165
86.8%

지휘관할_발생건수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct132
Distinct (%)69.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean556.88947
Minimum0
Maximum14484
Zeros2
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T08:01:17.882831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q115.25
median61.5
Q3233.75
95-th percentile2310.85
Maximum14484
Range14484
Interquartile range (IQR)218.5

Descriptive statistics

Standard deviation1865.2404
Coefficient of variation (CV)3.3493906
Kurtosis34.775532
Mean556.88947
Median Absolute Deviation (MAD)55.5
Skewness5.6510831
Sum105809
Variance3479121.6
MonotonicityNot monotonic
2023-12-13T08:01:18.077366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6
 
3.2%
4 6
 
3.2%
2 5
 
2.6%
6 5
 
2.6%
3 4
 
2.1%
8 4
 
2.1%
18 4
 
2.1%
38 4
 
2.1%
9 4
 
2.1%
11 3
 
1.6%
Other values (122) 145
76.3%
ValueCountFrequency (%)
0 2
 
1.1%
1 6
3.2%
2 5
2.6%
3 4
2.1%
4 6
3.2%
5 1
 
0.5%
6 5
2.6%
7 1
 
0.5%
8 4
2.1%
9 4
2.1%
ValueCountFrequency (%)
14484 1
0.5%
13882 1
0.5%
9694 1
0.5%
9077 1
0.5%
7322 1
0.5%
4015 1
0.5%
3465 1
0.5%
3325 1
0.5%
2890 1
0.5%
2431 1
0.5%

지휘관할_검거건수(건)
Real number (ℝ)

HIGH CORRELATION 

Distinct122
Distinct (%)64.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean489.74737
Minimum1
Maximum14502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T08:01:18.278329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q113
median50
Q3210.5
95-th percentile1790.75
Maximum14502
Range14501
Interquartile range (IQR)197.5

Descriptive statistics

Standard deviation1685.6302
Coefficient of variation (CV)3.4418361
Kurtosis39.805187
Mean489.74737
Median Absolute Deviation (MAD)45
Skewness5.9833635
Sum93052
Variance2841349.1
MonotonicityNot monotonic
2023-12-13T08:01:18.451957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7
 
3.7%
2 6
 
3.2%
4 5
 
2.6%
3 4
 
2.1%
8 4
 
2.1%
7 4
 
2.1%
6 4
 
2.1%
21 4
 
2.1%
16 3
 
1.6%
45 3
 
1.6%
Other values (112) 146
76.8%
ValueCountFrequency (%)
1 7
3.7%
2 6
3.2%
3 4
2.1%
4 5
2.6%
5 3
1.6%
6 4
2.1%
7 4
2.1%
8 4
2.1%
9 3
1.6%
10 2
 
1.1%
ValueCountFrequency (%)
14502 1
0.5%
11830 1
0.5%
8934 1
0.5%
7018 1
0.5%
6218 1
0.5%
3896 1
0.5%
3404 1
0.5%
3173 1
0.5%
2092 1
0.5%
1811 1
0.5%

지휘관할_남자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct124
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean430.67895
Minimum0
Maximum12870
Zeros3
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T08:01:18.642000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q114
median54
Q3202.5
95-th percentile1450.25
Maximum12870
Range12870
Interquartile range (IQR)188.5

Descriptive statistics

Standard deviation1460.9779
Coefficient of variation (CV)3.3922667
Kurtosis39.394705
Mean430.67895
Median Absolute Deviation (MAD)48
Skewness5.9291546
Sum81829
Variance2134456.3
MonotonicityNot monotonic
2023-12-13T08:01:18.825741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 9
 
4.7%
1 8
 
4.2%
8 6
 
3.2%
2 5
 
2.6%
3 4
 
2.1%
23 4
 
2.1%
19 4
 
2.1%
46 3
 
1.6%
43 3
 
1.6%
31 3
 
1.6%
Other values (114) 141
74.2%
ValueCountFrequency (%)
0 3
 
1.6%
1 8
4.2%
2 5
2.6%
3 4
2.1%
4 9
4.7%
5 1
 
0.5%
6 3
 
1.6%
7 1
 
0.5%
8 6
3.2%
10 2
 
1.1%
ValueCountFrequency (%)
12870 1
0.5%
8824 1
0.5%
8354 1
0.5%
7117 1
0.5%
4815 1
0.5%
3548 1
0.5%
3190 1
0.5%
2642 1
0.5%
1949 1
0.5%
1583 1
0.5%

지휘관할_여자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct76
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.657895
Minimum0
Maximum2638
Zeros31
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T08:01:18.989069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q339
95-th percentile337.1
Maximum2638
Range2638
Interquartile range (IQR)37

Descriptive statistics

Standard deviation325.58631
Coefficient of variation (CV)3.36844
Kurtosis35.738781
Mean96.657895
Median Absolute Deviation (MAD)9
Skewness5.7144595
Sum18365
Variance106006.45
MonotonicityNot monotonic
2023-12-13T08:01:19.175395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31
 
16.3%
2 14
 
7.4%
1 14
 
7.4%
6 10
 
5.3%
4 7
 
3.7%
3 6
 
3.2%
5 6
 
3.2%
9 5
 
2.6%
20 5
 
2.6%
19 4
 
2.1%
Other values (66) 88
46.3%
ValueCountFrequency (%)
0 31
16.3%
1 14
7.4%
2 14
7.4%
3 6
 
3.2%
4 7
 
3.7%
5 6
 
3.2%
6 10
 
5.3%
7 2
 
1.1%
8 1
 
0.5%
9 5
 
2.6%
ValueCountFrequency (%)
2638 1
0.5%
2296 1
0.5%
1776 1
0.5%
1624 1
0.5%
993 1
0.5%
952 1
0.5%
581 1
0.5%
518 1
0.5%
392 1
0.5%
338 1
0.5%

지휘관할_미상검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7473684
Minimum0
Maximum203
Zeros112
Zeros (%)58.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T08:01:19.324590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.75
95-th percentile27.2
Maximum203
Range203
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation23.430396
Coefficient of variation (CV)3.4725236
Kurtosis34.9728
Mean6.7473684
Median Absolute Deviation (MAD)0
Skewness5.5486392
Sum1282
Variance548.98346
MonotonicityNot monotonic
2023-12-13T08:01:19.457658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 112
58.9%
1 19
 
10.0%
2 11
 
5.8%
5 7
 
3.7%
4 5
 
2.6%
8 4
 
2.1%
9 4
 
2.1%
3 4
 
2.1%
12 2
 
1.1%
11 2
 
1.1%
Other values (19) 20
 
10.5%
ValueCountFrequency (%)
0 112
58.9%
1 19
 
10.0%
2 11
 
5.8%
3 4
 
2.1%
4 5
 
2.6%
5 7
 
3.7%
6 1
 
0.5%
7 1
 
0.5%
8 4
 
2.1%
9 4
 
2.1%
ValueCountFrequency (%)
203 1
0.5%
122 1
0.5%
119 1
0.5%
113 2
1.1%
66 1
0.5%
58 1
0.5%
49 1
0.5%
41 1
0.5%
29 1
0.5%
25 1
0.5%

지휘관할_법인(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0421053
Minimum0
Maximum427
Zeros113
Zeros (%)59.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T08:01:19.601929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile28.55
Maximum427
Range427
Interquartile range (IQR)3

Descriptive statistics

Standard deviation35.640597
Coefficient of variation (CV)4.4317497
Kurtosis104.44148
Mean8.0421053
Median Absolute Deviation (MAD)0
Skewness9.4730914
Sum1528
Variance1270.2522
MonotonicityNot monotonic
2023-12-13T08:01:19.728388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 113
59.5%
1 16
 
8.4%
2 11
 
5.8%
3 8
 
4.2%
4 5
 
2.6%
5 4
 
2.1%
11 3
 
1.6%
16 3
 
1.6%
15 2
 
1.1%
14 2
 
1.1%
Other values (23) 23
 
12.1%
ValueCountFrequency (%)
0 113
59.5%
1 16
 
8.4%
2 11
 
5.8%
3 8
 
4.2%
4 5
 
2.6%
5 4
 
2.1%
6 1
 
0.5%
7 1
 
0.5%
9 1
 
0.5%
11 3
 
1.6%
ValueCountFrequency (%)
427 1
0.5%
167 1
0.5%
138 1
0.5%
59 1
0.5%
55 1
0.5%
50 1
0.5%
49 1
0.5%
47 1
0.5%
44 1
0.5%
29 1
0.5%

Interactions

2023-12-13T08:01:13.858937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:01.503084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:02.607425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:03.747533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:04.786487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:06.130750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:07.062398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:08.025836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.168119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:10.297767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:11.725725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:12.709040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:13.941074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:01.586236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:02.692348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:03.823407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:05.150582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:06.200929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:07.155635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:08.101997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.257056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:10.716591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:11.813743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:12.790122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:14.016070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:01.661008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:02.790409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:03.897259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:05.255296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:06.274617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:07.232705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:08.178868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.339688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:10.804661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:11.900783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:12.870665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:14.088289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:01.752744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:02.884813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:03.986567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:05.339211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:06.343204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:07.309942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:08.256941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.424027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:10.896357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:11.981586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:12.955161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:14.166782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:01.839191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:02.970374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:04.085062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:05.426692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:06.426672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:07.385504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:08.334186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.517346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:10.977468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:12.061159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:13.043818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:14.252007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:01.926826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:03.046892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:04.169232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:05.504466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:06.497454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:07.462260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:08.416178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.606965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:11.069142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:12.136064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:13.158903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:14.360574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:02.031837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:03.151038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:04.304549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:05.595034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:06.592671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:07.543426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:08.507087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.699553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:11.152917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:12.218526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:13.284620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:14.449259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:02.138162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:03.255099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:04.389900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:05.692878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:06.671577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:07.625215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:08.608616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.794837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:11.237002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:12.307956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:13.383196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:14.561360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:02.243931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:03.344905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:04.474630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:05.804644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:06.751419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:07.708519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:08.728383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.888049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:11.334303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:12.391534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:13.473075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:14.675588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:02.339179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:03.462719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:04.553550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:05.896697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:06.823573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:07.791458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:08.817681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.972120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:11.434839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:12.468814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:13.564699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:14.775972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:02.437535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:03.546489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:04.631542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:05.969026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:06.892523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:07.865858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:08.914276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:10.093271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:11.535310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:12.549773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:13.653849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:14.886731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:02.524206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:03.662814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:04.714206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:06.057741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:06.989542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:07.953223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.058240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:10.212317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:11.645558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:12.633635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:13.761172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:01:19.838598image/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)1.0000.9870.9240.8780.8060.9170.5290.8460.7710.9200.8620.508
직수_인지_검거건수(건)0.9871.0000.8820.9140.8030.9240.4960.8190.7220.8780.8310.389
직수_인지_남자검거인원(명)0.9240.8821.0000.9480.8100.7750.7280.6820.5800.7990.6220.542
직수_인지_여자검거인원(명)0.8780.9140.9481.0000.8660.8000.7320.6330.5880.7080.6170.258
직수_인지_미상검거인원(명)0.8060.8030.8100.8661.0000.6640.6320.4760.3390.5270.8050.000
직수_인지_법인(개)0.9170.9240.7750.8000.6641.0000.5140.8000.7610.8510.7430.665
지휘관할_발생건수(건)0.5290.4960.7280.7320.6320.5141.0000.9320.8970.8340.5680.013
지휘관할_검거건수(건)0.8460.8190.6820.6330.4760.8000.9321.0000.9880.9770.6830.220
지휘관할_남자검거인원(명)0.7710.7220.5800.5880.3390.7610.8970.9881.0000.9730.6610.220
지휘관할_여자검거인원(명)0.9200.8780.7990.7080.5270.8510.8340.9770.9731.0000.7570.508
지휘관할_미상검거인원(명)0.8620.8310.6220.6170.8050.7430.5680.6830.6610.7571.0000.394
지휘관할_법인(개)0.5080.3890.5420.2580.0000.6650.0130.2200.2200.5080.3941.000
2023-12-13T08:01:20.024477image/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)1.0000.9890.8730.6820.5850.3880.5470.5430.5850.5310.5450.215
직수_인지_검거건수(건)0.9891.0000.8700.6860.5730.4020.5490.5460.5890.5240.5400.233
직수_인지_남자검거인원(명)0.8730.8701.0000.5840.5130.3530.5550.5530.6290.5100.4800.215
직수_인지_여자검거인원(명)0.6820.6860.5841.0000.5100.3520.4500.4620.4670.5660.4790.243
직수_인지_미상검거인원(명)0.5850.5730.5130.5101.0000.2430.3690.3740.3960.4210.7000.102
직수_인지_법인(개)0.3880.4020.3530.3520.2431.0000.2260.2340.2570.2040.3380.540
지휘관할_발생건수(건)0.5470.5490.5550.4500.3690.2261.0000.9900.9480.8060.5580.342
지휘관할_검거건수(건)0.5430.5460.5530.4620.3740.2340.9901.0000.9570.8070.5640.363
지휘관할_남자검거인원(명)0.5850.5890.6290.4670.3960.2570.9480.9571.0000.7960.5730.372
지휘관할_여자검거인원(명)0.5310.5240.5100.5660.4210.2040.8060.8070.7961.0000.5700.319
지휘관할_미상검거인원(명)0.5450.5400.4800.4790.7000.3380.5580.5640.5730.5701.0000.372
지휘관할_법인(개)0.2150.2330.2150.2430.1020.5400.3420.3630.3720.3190.3721.000

Missing values

2023-12-13T08:01:15.057802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:01:15.265242image/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)
0절도1199161969462183548993152
1불법사용223000524037000
2침입절도3330006736322132000
3장물1110009322431900
4사기2832392735229513882118308824263811324
5컴퓨터등사용사기000000260102862520
6부당이득000000222000
7편의시설부정이용00000010782892025
8전기통신금융사기피해금환급에관한특별법000000230130602400
9보험사기방지특별법000000312526900
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)
180특가법(도주차량)0010002132062044210
181특허법000000443102
182폐기물관리법15010116716515911559
183풍속영업의 규제에 관한법률000000445200
184하천법110000292831710
185학원의설립운영 및 과외교습에 관한법률000000383884000
186화물자동차 운수사업법222000636290502
187화재예방·소방시설설치유지 및 안전관리에 관한법률000000011000
188화학물질관리법000000101100859055
189기타특별법1151091122061321602092194958158167