Overview

Dataset statistics

Number of variables13
Number of observations174
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.8 KiB
Average record size in memory116.8 B

Variable types

Text1
Numeric12

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 서울동부지방검찰청이 관할하는 범죄 발생 검거상황과 관련된 통계임
Author대검찰청
URLhttps://www.data.go.kr/data/15084754/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 6 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 2 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 6 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 84 (48.3%) zerosZeros
직수_인지_검거건수(건) has 88 (50.6%) zerosZeros
직수_인지_남자검거인원(명) has 95 (54.6%) zerosZeros
직수_인지_여자검거인원(명) has 136 (78.2%) zerosZeros
직수_인지_미상검거인원(명) has 147 (84.5%) zerosZeros
직수_인지_법인(개) has 157 (90.2%) zerosZeros
지휘관할_검거건수(건) has 2 (1.1%) zerosZeros
지휘관할_남자검거인원(명) has 6 (3.4%) zerosZeros
지휘관할_여자검거인원(명) has 34 (19.5%) zerosZeros
지휘관할_미상검거인원(명) has 108 (62.1%) zerosZeros
지휘관할_법인(개) has 117 (67.2%) zerosZeros

Reproduction

Analysis started2023-12-12 20:40:19.128406
Analysis finished2023-12-12 20:40:35.611766
Duration16.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct174
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-13T05:40:35.767338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length8.0114943
Min length2

Characters and Unicode

Total characters1394
Distinct characters228
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

Unique174 ?
Unique (%)100.0%

Sample

1st row절도
2nd row불법사용
3rd row침입절도
4th row장물
5th row사기
ValueCountFrequency (%)
관한법률 21
 
7.8%
16
 
5.9%
마약류관리에 3
 
1.1%
보호에 2
 
0.7%
규제에 2
 
0.7%
처벌등에 2
 
0.7%
성보호에 2
 
0.7%
규제 2
 
0.7%
아동·청소년의 2
 
0.7%
관리에 2
 
0.7%
Other values (215) 215
79.9%
2023-12-13T05:40:36.141573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
100
 
7.2%
95
 
6.8%
49
 
3.5%
31
 
2.2%
30
 
2.2%
26
 
1.9%
24
 
1.7%
22
 
1.6%
22
 
1.6%
22
 
1.6%
Other values (218) 973
69.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1252
89.8%
Space Separator 95
 
6.8%
Other Punctuation 17
 
1.2%
Close Punctuation 15
 
1.1%
Open Punctuation 15
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
100
 
8.0%
49
 
3.9%
31
 
2.5%
30
 
2.4%
26
 
2.1%
24
 
1.9%
22
 
1.8%
22
 
1.8%
22
 
1.8%
21
 
1.7%
Other values (212) 905
72.3%
Other Punctuation
ValueCountFrequency (%)
, 10
58.8%
· 5
29.4%
/ 2
 
11.8%
Space Separator
ValueCountFrequency (%)
95
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1252
89.8%
Common 142
 
10.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
100
 
8.0%
49
 
3.9%
31
 
2.5%
30
 
2.4%
26
 
2.1%
24
 
1.9%
22
 
1.8%
22
 
1.8%
22
 
1.8%
21
 
1.7%
Other values (212) 905
72.3%
Common
ValueCountFrequency (%)
95
66.9%
) 15
 
10.6%
( 15
 
10.6%
, 10
 
7.0%
· 5
 
3.5%
/ 2
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1252
89.8%
ASCII 137
 
9.8%
None 5
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
100
 
8.0%
49
 
3.9%
31
 
2.5%
30
 
2.4%
26
 
2.1%
24
 
1.9%
22
 
1.8%
22
 
1.8%
22
 
1.8%
21
 
1.7%
Other values (212) 905
72.3%
ASCII
ValueCountFrequency (%)
95
69.3%
) 15
 
10.9%
( 15
 
10.9%
, 10
 
7.3%
/ 2
 
1.5%
None
ValueCountFrequency (%)
· 5
100.0%

직수_인지_발생건수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3218391
Minimum0
Maximum533
Zeros84
Zeros (%)48.3%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T05:40:36.328643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile31.45
Maximum533
Range533
Interquartile range (IQR)4

Descriptive statistics

Standard deviation43.270087
Coefficient of variation (CV)4.6417973
Kurtosis125.90411
Mean9.3218391
Median Absolute Deviation (MAD)1
Skewness10.599632
Sum1622
Variance1872.3004
MonotonicityNot monotonic
2023-12-13T05:40:36.467029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 84
48.3%
1 25
 
14.4%
2 9
 
5.2%
3 9
 
5.2%
8 5
 
2.9%
7 5
 
2.9%
4 4
 
2.3%
5 4
 
2.3%
9 3
 
1.7%
10 3
 
1.7%
Other values (19) 23
 
13.2%
ValueCountFrequency (%)
0 84
48.3%
1 25
 
14.4%
2 9
 
5.2%
3 9
 
5.2%
4 4
 
2.3%
5 4
 
2.3%
6 1
 
0.6%
7 5
 
2.9%
8 5
 
2.9%
9 3
 
1.7%
ValueCountFrequency (%)
533 1
0.6%
135 1
0.6%
83 1
0.6%
76 1
0.6%
72 1
0.6%
66 1
0.6%
53 1
0.6%
52 1
0.6%
36 1
0.6%
29 2
1.1%

직수_인지_검거건수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8563218
Minimum0
Maximum456
Zeros88
Zeros (%)50.6%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T05:40:36.636545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile22
Maximum456
Range456
Interquartile range (IQR)3

Descriptive statistics

Standard deviation35.79949
Coefficient of variation (CV)5.2213842
Kurtosis145.35271
Mean6.8563218
Median Absolute Deviation (MAD)0
Skewness11.632423
Sum1193
Variance1281.6035
MonotonicityNot monotonic
2023-12-13T05:40:36.769172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 88
50.6%
1 27
 
15.5%
3 10
 
5.7%
2 9
 
5.2%
8 5
 
2.9%
6 3
 
1.7%
5 3
 
1.7%
12 3
 
1.7%
22 3
 
1.7%
7 3
 
1.7%
Other values (14) 20
 
11.5%
ValueCountFrequency (%)
0 88
50.6%
1 27
 
15.5%
2 9
 
5.2%
3 10
 
5.7%
4 2
 
1.1%
5 3
 
1.7%
6 3
 
1.7%
7 3
 
1.7%
8 5
 
2.9%
9 1
 
0.6%
ValueCountFrequency (%)
456 1
 
0.6%
70 1
 
0.6%
63 1
 
0.6%
56 1
 
0.6%
51 1
 
0.6%
41 1
 
0.6%
24 2
1.1%
22 3
1.7%
19 2
1.1%
17 1
 
0.6%

직수_인지_남자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4482759
Minimum0
Maximum941
Zeros95
Zeros (%)54.6%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T05:40:36.931197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile25.7
Maximum941
Range941
Interquartile range (IQR)3

Descriptive statistics

Standard deviation71.779006
Coefficient of variation (CV)7.597048
Kurtosis166.72415
Mean9.4482759
Median Absolute Deviation (MAD)0
Skewness12.790761
Sum1644
Variance5152.2256
MonotonicityNot monotonic
2023-12-13T05:40:37.070927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 95
54.6%
1 20
 
11.5%
2 12
 
6.9%
4 9
 
5.2%
3 5
 
2.9%
5 4
 
2.3%
10 4
 
2.3%
14 3
 
1.7%
7 3
 
1.7%
16 2
 
1.1%
Other values (16) 17
 
9.8%
ValueCountFrequency (%)
0 95
54.6%
1 20
 
11.5%
2 12
 
6.9%
3 5
 
2.9%
4 9
 
5.2%
5 4
 
2.3%
6 1
 
0.6%
7 3
 
1.7%
8 1
 
0.6%
9 2
 
1.1%
ValueCountFrequency (%)
941 1
0.6%
85 1
0.6%
55 1
0.6%
44 1
0.6%
41 1
0.6%
34 1
0.6%
33 1
0.6%
30 1
0.6%
27 1
0.6%
25 1
0.6%

직수_인지_여자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2183908
Minimum0
Maximum100
Zeros136
Zeros (%)78.2%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T05:40:37.198977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.8805024
Coefficient of variation (CV)6.4679596
Kurtosis145.03067
Mean1.2183908
Median Absolute Deviation (MAD)0
Skewness11.66009
Sum212
Variance62.102319
MonotonicityNot monotonic
2023-12-13T05:40:37.345868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 136
78.2%
1 20
 
11.5%
2 8
 
4.6%
3 2
 
1.1%
100 1
 
0.6%
9 1
 
0.6%
13 1
 
0.6%
4 1
 
0.6%
6 1
 
0.6%
22 1
 
0.6%
Other values (2) 2
 
1.1%
ValueCountFrequency (%)
0 136
78.2%
1 20
 
11.5%
2 8
 
4.6%
3 2
 
1.1%
4 1
 
0.6%
5 1
 
0.6%
6 1
 
0.6%
9 1
 
0.6%
11 1
 
0.6%
13 1
 
0.6%
ValueCountFrequency (%)
100 1
 
0.6%
22 1
 
0.6%
13 1
 
0.6%
11 1
 
0.6%
9 1
 
0.6%
6 1
 
0.6%
5 1
 
0.6%
4 1
 
0.6%
3 2
 
1.1%
2 8
4.6%

직수_인지_미상검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0862069
Minimum0
Maximum80
Zeros147
Zeros (%)84.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T05:40:37.482664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6.7
Maximum80
Range80
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.2429342
Coefficient of variation (CV)4.4304974
Kurtosis40.531814
Mean2.0862069
Median Absolute Deviation (MAD)0
Skewness6.0599732
Sum363
Variance85.431832
MonotonicityNot monotonic
2023-12-13T05:40:37.612445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 147
84.5%
2 5
 
2.9%
1 5
 
2.9%
4 3
 
1.7%
6 2
 
1.1%
3 2
 
1.1%
31 1
 
0.6%
22 1
 
0.6%
30 1
 
0.6%
9 1
 
0.6%
Other values (6) 6
 
3.4%
ValueCountFrequency (%)
0 147
84.5%
1 5
 
2.9%
2 5
 
2.9%
3 2
 
1.1%
4 3
 
1.7%
5 1
 
0.6%
6 2
 
1.1%
8 1
 
0.6%
9 1
 
0.6%
22 1
 
0.6%
ValueCountFrequency (%)
80 1
0.6%
55 1
0.6%
53 1
0.6%
31 1
0.6%
30 1
0.6%
25 1
0.6%
22 1
0.6%
9 1
0.6%
8 1
0.6%
6 2
1.1%

직수_인지_법인(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35632184
Minimum0
Maximum17
Zeros157
Zeros (%)90.2%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T05:40:37.738087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6445883
Coefficient of variation (CV)4.6154576
Kurtosis65.132826
Mean0.35632184
Median Absolute Deviation (MAD)0
Skewness7.3723723
Sum62
Variance2.7046708
MonotonicityNot monotonic
2023-12-13T05:40:37.848906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 157
90.2%
2 8
 
4.6%
1 4
 
2.3%
17 1
 
0.6%
6 1
 
0.6%
8 1
 
0.6%
7 1
 
0.6%
4 1
 
0.6%
ValueCountFrequency (%)
0 157
90.2%
1 4
 
2.3%
2 8
 
4.6%
4 1
 
0.6%
6 1
 
0.6%
7 1
 
0.6%
8 1
 
0.6%
17 1
 
0.6%
ValueCountFrequency (%)
17 1
 
0.6%
8 1
 
0.6%
7 1
 
0.6%
6 1
 
0.6%
4 1
 
0.6%
2 8
 
4.6%
1 4
 
2.3%
0 157
90.2%

지휘관할_발생건수(건)
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean312.03448
Minimum0
Maximum7961
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T05:40:37.998220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16.25
median27
Q3132.25
95-th percentile1152.35
Maximum7961
Range7961
Interquartile range (IQR)126

Descriptive statistics

Standard deviation1052.686
Coefficient of variation (CV)3.3736209
Kurtosis31.628251
Mean312.03448
Median Absolute Deviation (MAD)25
Skewness5.4537213
Sum54294
Variance1108147.9
MonotonicityNot monotonic
2023-12-13T05:40:38.137013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 11
 
6.3%
5 9
 
5.2%
2 9
 
5.2%
9 5
 
2.9%
12 5
 
2.9%
6 5
 
2.9%
14 5
 
2.9%
3 5
 
2.9%
10 4
 
2.3%
4 4
 
2.3%
Other values (90) 112
64.4%
ValueCountFrequency (%)
0 1
 
0.6%
1 11
6.3%
2 9
5.2%
3 5
2.9%
4 4
 
2.3%
5 9
5.2%
6 5
2.9%
7 1
 
0.6%
8 2
 
1.1%
9 5
2.9%
ValueCountFrequency (%)
7961 1
0.6%
7025 1
0.6%
5605 1
0.6%
5540 1
0.6%
3209 1
0.6%
3010 1
0.6%
1702 1
0.6%
1381 1
0.6%
1205 1
0.6%
1124 1
0.6%

지휘관할_검거건수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct93
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245.21839
Minimum0
Maximum6024
Zeros2
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T05:40:38.299075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.25
median23
Q388.5
95-th percentile1000.35
Maximum6024
Range6024
Interquartile range (IQR)83.25

Descriptive statistics

Standard deviation821.74581
Coefficient of variation (CV)3.3510774
Kurtosis31.642635
Mean245.21839
Median Absolute Deviation (MAD)20
Skewness5.4748253
Sum42668
Variance675266.17
MonotonicityNot monotonic
2023-12-13T05:40:38.439755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 15
 
8.6%
5 9
 
5.2%
2 7
 
4.0%
14 7
 
4.0%
3 6
 
3.4%
13 5
 
2.9%
4 5
 
2.9%
11 5
 
2.9%
8 4
 
2.3%
23 4
 
2.3%
Other values (83) 107
61.5%
ValueCountFrequency (%)
0 2
 
1.1%
1 15
8.6%
2 7
4.0%
3 6
 
3.4%
4 5
 
2.9%
5 9
5.2%
6 3
 
1.7%
7 3
 
1.7%
8 4
 
2.3%
9 3
 
1.7%
ValueCountFrequency (%)
6024 1
0.6%
5357 1
0.6%
5283 1
0.6%
3512 1
0.6%
3272 1
0.6%
1255 1
0.6%
1151 1
0.6%
1121 1
0.6%
1079 1
0.6%
958 1
0.6%

지휘관할_남자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct98
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246.90805
Minimum0
Maximum6785
Zeros6
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T05:40:38.588049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median25.5
Q399.5
95-th percentile901.4
Maximum6785
Range6785
Interquartile range (IQR)92.5

Descriptive statistics

Standard deviation842.53685
Coefficient of variation (CV)3.4123507
Kurtosis40.248019
Mean246.90805
Median Absolute Deviation (MAD)22.5
Skewness6.064518
Sum42962
Variance709868.34
MonotonicityNot monotonic
2023-12-13T05:40:38.740906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 11
 
6.3%
6 8
 
4.6%
7 7
 
4.0%
4 6
 
3.4%
14 6
 
3.4%
0 6
 
3.4%
2 5
 
2.9%
13 4
 
2.3%
3 4
 
2.3%
10 4
 
2.3%
Other values (88) 113
64.9%
ValueCountFrequency (%)
0 6
3.4%
1 11
6.3%
2 5
2.9%
3 4
 
2.3%
4 6
3.4%
5 3
 
1.7%
6 8
4.6%
7 7
4.0%
8 1
 
0.6%
9 3
 
1.7%
ValueCountFrequency (%)
6785 1
0.6%
6373 1
0.6%
4491 1
0.6%
2985 1
0.6%
2510 1
0.6%
1313 1
0.6%
1151 1
0.6%
1097 1
0.6%
917 1
0.6%
893 1
0.6%

지휘관할_여자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)36.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.551724
Minimum0
Maximum1686
Zeros34
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T05:40:38.881960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q328.5
95-th percentile251.2
Maximum1686
Range1686
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation200.58667
Coefficient of variation (CV)3.31265
Kurtosis35.636975
Mean60.551724
Median Absolute Deviation (MAD)4
Skewness5.6555887
Sum10536
Variance40235.012
MonotonicityNot monotonic
2023-12-13T05:40:39.024957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34
19.5%
1 28
16.1%
3 11
 
6.3%
2 9
 
5.2%
4 7
 
4.0%
9 6
 
3.4%
5 5
 
2.9%
13 4
 
2.3%
7 4
 
2.3%
18 4
 
2.3%
Other values (53) 62
35.6%
ValueCountFrequency (%)
0 34
19.5%
1 28
16.1%
2 9
 
5.2%
3 11
 
6.3%
4 7
 
4.0%
5 5
 
2.9%
6 3
 
1.7%
7 4
 
2.3%
8 1
 
0.6%
9 6
 
3.4%
ValueCountFrequency (%)
1686 1
0.6%
1200 1
0.6%
1059 1
0.6%
908 1
0.6%
783 1
0.6%
328 1
0.6%
322 1
0.6%
274 1
0.6%
272 1
0.6%
240 1
0.6%

지휘관할_미상검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0862069
Minimum0
Maximum152
Zeros108
Zeros (%)62.1%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T05:40:39.151677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile34
Maximum152
Range152
Interquartile range (IQR)2

Descriptive statistics

Standard deviation21.925743
Coefficient of variation (CV)3.60253
Kurtosis29.891756
Mean6.0862069
Median Absolute Deviation (MAD)0
Skewness5.2689035
Sum1059
Variance480.73819
MonotonicityNot monotonic
2023-12-13T05:40:39.261921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 108
62.1%
1 20
 
11.5%
2 14
 
8.0%
3 9
 
5.2%
34 2
 
1.1%
9 2
 
1.1%
5 2
 
1.1%
6 1
 
0.6%
37 1
 
0.6%
68 1
 
0.6%
Other values (14) 14
 
8.0%
ValueCountFrequency (%)
0 108
62.1%
1 20
 
11.5%
2 14
 
8.0%
3 9
 
5.2%
5 2
 
1.1%
6 1
 
0.6%
7 1
 
0.6%
9 2
 
1.1%
10 1
 
0.6%
11 1
 
0.6%
ValueCountFrequency (%)
152 1
0.6%
147 1
0.6%
144 1
0.6%
84 1
0.6%
68 1
0.6%
57 1
0.6%
41 1
0.6%
37 1
0.6%
34 2
1.1%
32 1
0.6%

지휘관할_법인(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0114943
Minimum0
Maximum137
Zeros117
Zeros (%)67.2%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T05:40:39.393118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile7.35
Maximum137
Range137
Interquartile range (IQR)1

Descriptive statistics

Standard deviation12.532957
Coefficient of variation (CV)4.1617071
Kurtosis78.566622
Mean3.0114943
Median Absolute Deviation (MAD)0
Skewness8.1130992
Sum524
Variance157.07501
MonotonicityNot monotonic
2023-12-13T05:40:39.525788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 117
67.2%
1 14
 
8.0%
2 12
 
6.9%
7 6
 
3.4%
4 5
 
2.9%
3 5
 
2.9%
5 3
 
1.7%
6 3
 
1.7%
44 2
 
1.1%
24 1
 
0.6%
Other values (6) 6
 
3.4%
ValueCountFrequency (%)
0 117
67.2%
1 14
 
8.0%
2 12
 
6.9%
3 5
 
2.9%
4 5
 
2.9%
5 3
 
1.7%
6 3
 
1.7%
7 6
 
3.4%
8 1
 
0.6%
11 1
 
0.6%
ValueCountFrequency (%)
137 1
 
0.6%
53 1
 
0.6%
44 2
 
1.1%
41 1
 
0.6%
24 1
 
0.6%
14 1
 
0.6%
11 1
 
0.6%
8 1
 
0.6%
7 6
3.4%
6 3
1.7%

Interactions

2023-12-13T05:40:33.833066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:19.573381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:20.672476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:22.255058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:23.744214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:25.081379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:26.631003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:28.036695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:29.349354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:30.303106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:31.389935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:32.714810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:33.921213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:19.663504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:20.778182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:22.373274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:23.862807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:25.213243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:26.758030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:28.129510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:29.425430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:30.379275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:31.484658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:32.807789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:33.998271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:19.745080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:20.873034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:22.510144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:23.978638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:25.329843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:26.861355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:28.213057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:29.493384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:30.447644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:31.571696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:32.890532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:34.336586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:19.824331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:20.964495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:22.619145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:24.082887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:25.450790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:26.979159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:28.296052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:29.567818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:30.518616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:31.661582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:32.979271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:34.416441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:19.905277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:21.046071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:22.754199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:24.183586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:25.576041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:27.086965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:28.396086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:29.643927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:30.600090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:31.748471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:33.065355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:34.513464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:20.001555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:21.154981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:22.877359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:24.282101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:25.688327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:27.194614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:28.492528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:29.727179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:30.692961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:31.856400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:33.187446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:34.616532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:20.093334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:21.259336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:23.017889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:24.386053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:25.822912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:27.297913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:28.579058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:29.806930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:30.808314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:31.968960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:33.287048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:34.721483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:20.195236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:21.351413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:23.150817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:24.494133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:25.985244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:27.447042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:28.670678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:29.903365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:30.907284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:32.079936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:33.386764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:34.824573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:20.289496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:21.443375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:23.258873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:24.618033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:26.092572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:27.587068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:28.753268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:29.984145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:31.000180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:32.185763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:33.472875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:34.927905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:20.392873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:21.549270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:23.372960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:24.747222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:26.218484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:27.722963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:29.096970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:30.067862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:31.095075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:32.325159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:33.568633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:35.019766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:20.492628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:21.657670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:23.502529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:24.859554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:26.353158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:27.833117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:29.177348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:30.147053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:31.198296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:32.442876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:33.655754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:35.113712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:20.577855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:21.786041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:23.627357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:24.963916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:26.487917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:27.943073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:29.263543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:30.227382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:31.297619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:32.548608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:33.747926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:40:39.621797image/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)1.0000.8771.0000.9750.8020.8100.7810.7910.5850.7540.6550.607
직수_인지_검거건수(건)0.8771.0001.0000.8600.9280.9710.9700.9710.8360.8250.6170.554
직수_인지_남자검거인원(명)1.0001.0001.0001.0000.8821.0001.0001.0000.8821.0000.5150.461
직수_인지_여자검거인원(명)0.9750.8601.0001.0000.8650.8360.7370.8360.5620.7970.6030.553
직수_인지_미상검거인원(명)0.8020.9280.8820.8651.0000.8990.5780.8900.5410.7110.8480.505
직수_인지_법인(개)0.8100.9711.0000.8360.8991.0000.7610.9350.7360.8010.5790.685
지휘관할_발생건수(건)0.7810.9701.0000.7370.5780.7611.0000.9750.9990.9400.6420.385
지휘관할_검거건수(건)0.7910.9711.0000.8360.8900.9350.9751.0000.9820.9340.5790.510
지휘관할_남자검거인원(명)0.5850.8360.8820.5620.5410.7360.9990.9821.0000.9130.5150.208
지휘관할_여자검거인원(명)0.7540.8251.0000.7970.7110.8010.9400.9340.9131.0000.8240.522
지휘관할_미상검거인원(명)0.6550.6170.5150.6030.8480.5790.6420.5790.5150.8241.0000.494
지휘관할_법인(개)0.6070.5540.4610.5530.5050.6850.3850.5100.2080.5220.4941.000
2023-12-13T05:40:40.091227image/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)1.0000.9790.8920.6190.5760.3900.6030.5780.5780.5390.5130.301
직수_인지_검거건수(건)0.9791.0000.8930.6320.5540.4010.5940.5710.5670.5400.5070.303
직수_인지_남자검거인원(명)0.8920.8931.0000.5760.4980.4100.5990.5840.5960.5450.4940.286
직수_인지_여자검거인원(명)0.6190.6320.5761.0000.4490.3550.4170.3980.3810.4410.3440.271
직수_인지_미상검거인원(명)0.5760.5540.4980.4491.0000.3620.3770.3510.3560.3600.6620.235
직수_인지_법인(개)0.3900.4010.4100.3550.3621.0000.2070.2030.1990.1440.2610.518
지휘관할_발생건수(건)0.6030.5940.5990.4170.3770.2071.0000.9710.9540.7890.5270.326
지휘관할_검거건수(건)0.5780.5710.5840.3980.3510.2030.9711.0000.9740.8120.5190.339
지휘관할_남자검거인원(명)0.5780.5670.5960.3810.3560.1990.9540.9741.0000.7990.5340.341
지휘관할_여자검거인원(명)0.5390.5400.5450.4410.3600.1440.7890.8120.7991.0000.5340.345
지휘관할_미상검거인원(명)0.5130.5070.4940.3440.6620.2610.5270.5190.5340.5341.0000.391
지휘관할_법인(개)0.3010.3030.2860.2710.2350.5180.3260.3390.3410.3450.3911.000

Missing values

2023-12-13T05:40:35.282367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:40:35.502694image/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)
0절도8641227025351225101059282
1불법사용00000010914000
2침입절도11000018517298900
3장물1110006138871300
4사기5334569411005517796160246785168614444
5컴퓨터등사용사기22200013963552010
6부당이득000000317300
7편의시설부정이용00000051410412
8전기통신금융사기피해금환급에관한특별법111000632218600
9보험사기방지특별법00100038511115300
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)
164특허법0000006512113
165폐기물관리법242210107262311107
166풍속영업의 규제에 관한법률000000131317900
167하천법000000221100
168학교보건법000000995400
169학원의설립운영 및 과외교습에 관한법률0000002424131500
170화물자동차 운수사업법000000133127160901
171화재예방·소방시설설치유지 및 안전관리에 관한법률000000113100
172화학물질관리법000000446100
173기타특별법836327112241381125510977833753