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 인천지방검찰청이 관할하는 범죄 발생의 검거상황과 관련된 통계임
Author대검찰청
URLhttps://www.data.go.kr/data/15084764/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 3 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 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 7 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 87 (45.8%) zerosZeros
직수_인지_여자검거인원(명) has 132 (69.5%) zerosZeros
직수_인지_미상검거인원(명) has 143 (75.3%) zerosZeros
직수_인지_법인(개) has 167 (87.9%) zerosZeros
지휘관할_남자검거인원(명) has 2 (1.1%) zerosZeros
지휘관할_여자검거인원(명) has 21 (11.1%) zerosZeros
지휘관할_미상검거인원(명) has 92 (48.4%) zerosZeros
지휘관할_법인(개) has 107 (56.3%) zerosZeros

Reproduction

Analysis started2023-12-12 20:29:39.974227
Analysis finished2023-12-12 20:30:00.460120
Duration20.49 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
2023-12-13T05:30:00.618946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length23.5
Mean length8.2947368
Min length2

Characters and Unicode

Total characters1576
Distinct characters242
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 27
 
8.7%
21
 
6.8%
관리에 4
 
1.3%
마약류관리에 3
 
1.0%
규제에 2
 
0.6%
규제 2
 
0.6%
관한 2
 
0.6%
처벌등에 2
 
0.6%
보호에 2
 
0.6%
아동·청소년의 2
 
0.6%
Other values (241) 243
78.4%
2023-12-13T05:30:01.043637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
120
 
7.6%
114
 
7.2%
63
 
4.0%
37
 
2.3%
37
 
2.3%
32
 
2.0%
29
 
1.8%
28
 
1.8%
25
 
1.6%
24
 
1.5%
Other values (232) 1067
67.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1408
89.3%
Space Separator 120
 
7.6%
Other Punctuation 18
 
1.1%
Close Punctuation 15
 
1.0%
Open Punctuation 15
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
114
 
8.1%
63
 
4.5%
37
 
2.6%
37
 
2.6%
32
 
2.3%
29
 
2.1%
28
 
2.0%
25
 
1.8%
24
 
1.7%
23
 
1.6%
Other values (226) 996
70.7%
Other Punctuation
ValueCountFrequency (%)
, 10
55.6%
· 6
33.3%
/ 2
 
11.1%
Space Separator
ValueCountFrequency (%)
120
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1408
89.3%
Common 168
 
10.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
114
 
8.1%
63
 
4.5%
37
 
2.6%
37
 
2.6%
32
 
2.3%
29
 
2.1%
28
 
2.0%
25
 
1.8%
24
 
1.7%
23
 
1.6%
Other values (226) 996
70.7%
Common
ValueCountFrequency (%)
120
71.4%
) 15
 
8.9%
( 15
 
8.9%
, 10
 
6.0%
· 6
 
3.6%
/ 2
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1408
89.3%
ASCII 162
 
10.3%
None 6
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
120
74.1%
) 15
 
9.3%
( 15
 
9.3%
, 10
 
6.2%
/ 2
 
1.2%
Hangul
ValueCountFrequency (%)
114
 
8.1%
63
 
4.5%
37
 
2.6%
37
 
2.6%
32
 
2.3%
29
 
2.1%
28
 
2.0%
25
 
1.8%
24
 
1.7%
23
 
1.6%
Other values (226) 996
70.7%
None
ValueCountFrequency (%)
· 6
100.0%

직수_인지_발생건수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.731579
Minimum0
Maximum750
Zeros76
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T05:30:01.250016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38.75
95-th percentile88.1
Maximum750
Range750
Interquartile range (IQR)8.75

Descriptive statistics

Standard deviation65.556362
Coefficient of variation (CV)3.9181217
Kurtosis87.127146
Mean16.731579
Median Absolute Deviation (MAD)1
Skewness8.5404791
Sum3179
Variance4297.6366
MonotonicityNot monotonic
2023-12-13T05:30:01.417493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 76
40.0%
1 28
 
14.7%
2 11
 
5.8%
4 9
 
4.7%
19 6
 
3.2%
3 5
 
2.6%
7 5
 
2.6%
12 4
 
2.1%
6 4
 
2.1%
10 3
 
1.6%
Other values (30) 39
20.5%
ValueCountFrequency (%)
0 76
40.0%
1 28
 
14.7%
2 11
 
5.8%
3 5
 
2.6%
4 9
 
4.7%
5 3
 
1.6%
6 4
 
2.1%
7 5
 
2.6%
8 1
 
0.5%
9 3
 
1.6%
ValueCountFrequency (%)
750 1
0.5%
348 1
0.5%
253 1
0.5%
151 1
0.5%
111 2
1.1%
100 1
0.5%
99 1
0.5%
92 1
0.5%
89 1
0.5%
87 1
0.5%

직수_인지_검거건수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.194737
Minimum0
Maximum388
Zeros75
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T05:30:01.603042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37
95-th percentile72.3
Maximum388
Range388
Interquartile range (IQR)7

Descriptive statistics

Standard deviation40.962998
Coefficient of variation (CV)3.3590719
Kurtosis47.882557
Mean12.194737
Median Absolute Deviation (MAD)1
Skewness6.3915983
Sum2317
Variance1677.9672
MonotonicityNot monotonic
2023-12-13T05:30:01.773990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 75
39.5%
1 35
18.4%
4 12
 
6.3%
2 7
 
3.7%
3 6
 
3.2%
11 4
 
2.1%
5 4
 
2.1%
10 4
 
2.1%
17 3
 
1.6%
16 3
 
1.6%
Other values (26) 37
19.5%
ValueCountFrequency (%)
0 75
39.5%
1 35
18.4%
2 7
 
3.7%
3 6
 
3.2%
4 12
 
6.3%
5 4
 
2.1%
6 2
 
1.1%
7 2
 
1.1%
8 3
 
1.6%
9 3
 
1.6%
ValueCountFrequency (%)
388 1
0.5%
248 1
0.5%
245 1
0.5%
104 1
0.5%
101 1
0.5%
94 1
0.5%
85 1
0.5%
83 1
0.5%
77 1
0.5%
75 1
0.5%

직수_인지_남자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.247368
Minimum0
Maximum699
Zeros87
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T05:30:01.910365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37
95-th percentile87.2
Maximum699
Range699
Interquartile range (IQR)7

Descriptive statistics

Standard deviation67.52046
Coefficient of variation (CV)3.9148268
Kurtosis66.904253
Mean17.247368
Median Absolute Deviation (MAD)1
Skewness7.6087791
Sum3277
Variance4559.0126
MonotonicityNot monotonic
2023-12-13T05:30:02.054864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 87
45.8%
1 19
 
10.0%
2 9
 
4.7%
3 8
 
4.2%
6 6
 
3.2%
4 6
 
3.2%
5 5
 
2.6%
13 4
 
2.1%
18 3
 
1.6%
9 3
 
1.6%
Other values (28) 40
21.1%
ValueCountFrequency (%)
0 87
45.8%
1 19
 
10.0%
2 9
 
4.7%
3 8
 
4.2%
4 6
 
3.2%
5 5
 
2.6%
6 6
 
3.2%
7 3
 
1.6%
8 2
 
1.1%
9 3
 
1.6%
ValueCountFrequency (%)
699 1
0.5%
480 1
0.5%
259 1
0.5%
139 1
0.5%
137 1
0.5%
125 1
0.5%
120 2
1.1%
103 1
0.5%
89 1
0.5%
85 1
0.5%

직수_인지_여자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0421053
Minimum0
Maximum162
Zeros132
Zeros (%)69.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T05:30:02.189197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile14.2
Maximum162
Range162
Interquartile range (IQR)1

Descriptive statistics

Standard deviation13.646261
Coefficient of variation (CV)4.4857951
Kurtosis100.20328
Mean3.0421053
Median Absolute Deviation (MAD)0
Skewness9.1786499
Sum578
Variance186.22044
MonotonicityNot monotonic
2023-12-13T05:30:02.322192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 132
69.5%
1 19
 
10.0%
2 13
 
6.8%
3 6
 
3.2%
6 3
 
1.6%
7 3
 
1.6%
33 1
 
0.5%
18 1
 
0.5%
25 1
 
0.5%
11 1
 
0.5%
Other values (10) 10
 
5.3%
ValueCountFrequency (%)
0 132
69.5%
1 19
 
10.0%
2 13
 
6.8%
3 6
 
3.2%
6 3
 
1.6%
7 3
 
1.6%
8 1
 
0.5%
9 1
 
0.5%
11 1
 
0.5%
12 1
 
0.5%
ValueCountFrequency (%)
162 1
0.5%
64 1
0.5%
35 1
0.5%
33 1
0.5%
30 1
0.5%
27 1
0.5%
26 1
0.5%
25 1
0.5%
18 1
0.5%
16 1
0.5%

직수_인지_미상검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5789474
Minimum0
Maximum135
Zeros143
Zeros (%)75.3%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T05:30:02.465194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7.1
Maximum135
Range135
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.53995
Coefficient of variation (CV)4.8624296
Kurtosis74.81703
Mean2.5789474
Median Absolute Deviation (MAD)0
Skewness8.0670664
Sum490
Variance157.25035
MonotonicityNot monotonic
2023-12-13T05:30:02.587706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 143
75.3%
1 18
 
9.5%
2 9
 
4.7%
4 3
 
1.6%
6 3
 
1.6%
3 3
 
1.6%
36 2
 
1.1%
23 1
 
0.5%
22 1
 
0.5%
14 1
 
0.5%
Other values (6) 6
 
3.2%
ValueCountFrequency (%)
0 143
75.3%
1 18
 
9.5%
2 9
 
4.7%
3 3
 
1.6%
4 3
 
1.6%
5 1
 
0.5%
6 3
 
1.6%
8 1
 
0.5%
12 1
 
0.5%
14 1
 
0.5%
ValueCountFrequency (%)
135 1
 
0.5%
82 1
 
0.5%
42 1
 
0.5%
36 2
1.1%
23 1
 
0.5%
22 1
 
0.5%
14 1
 
0.5%
12 1
 
0.5%
8 1
 
0.5%
6 3
1.6%

직수_인지_법인(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3631579
Minimum0
Maximum135
Zeros167
Zeros (%)87.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T05:30:02.713804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.55
Maximum135
Range135
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.46737
Coefficient of variation (CV)7.6787655
Kurtosis143.38902
Mean1.3631579
Median Absolute Deviation (MAD)0
Skewness11.513003
Sum259
Variance109.56583
MonotonicityNot monotonic
2023-12-13T05:30:02.832354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 167
87.9%
1 8
 
4.2%
2 5
 
2.6%
3 5
 
2.6%
25 1
 
0.5%
4 1
 
0.5%
135 1
 
0.5%
21 1
 
0.5%
41 1
 
0.5%
ValueCountFrequency (%)
0 167
87.9%
1 8
 
4.2%
2 5
 
2.6%
3 5
 
2.6%
4 1
 
0.5%
21 1
 
0.5%
25 1
 
0.5%
41 1
 
0.5%
135 1
 
0.5%
ValueCountFrequency (%)
135 1
 
0.5%
41 1
 
0.5%
25 1
 
0.5%
21 1
 
0.5%
4 1
 
0.5%
3 5
 
2.6%
2 5
 
2.6%
1 8
 
4.2%
0 167
87.9%

지휘관할_발생건수(건)
Real number (ℝ)

HIGH CORRELATION 

Distinct138
Distinct (%)72.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean796.20526
Minimum0
Maximum19181
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T05:30:02.978995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q118.25
median78
Q3338.25
95-th percentile3782.25
Maximum19181
Range19181
Interquartile range (IQR)320

Descriptive statistics

Standard deviation2505.2087
Coefficient of variation (CV)3.1464357
Kurtosis28.823746
Mean796.20526
Median Absolute Deviation (MAD)69.5
Skewness5.1540669
Sum151279
Variance6276070.6
MonotonicityNot monotonic
2023-12-13T05:30:03.141834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8
 
4.2%
9 4
 
2.1%
8 4
 
2.1%
11 4
 
2.1%
28 4
 
2.1%
2 4
 
2.1%
3 3
 
1.6%
22 3
 
1.6%
42 3
 
1.6%
16 3
 
1.6%
Other values (128) 150
78.9%
ValueCountFrequency (%)
0 1
 
0.5%
1 8
4.2%
2 4
2.1%
3 3
 
1.6%
4 2
 
1.1%
5 2
 
1.1%
7 2
 
1.1%
8 4
2.1%
9 4
2.1%
10 1
 
0.5%
ValueCountFrequency (%)
19181 1
0.5%
15498 1
0.5%
14073 1
0.5%
13703 1
0.5%
10214 1
0.5%
6258 1
0.5%
5583 1
0.5%
4912 1
0.5%
4550 1
0.5%
3969 1
0.5%

지휘관할_검거건수(건)
Real number (ℝ)

HIGH CORRELATION 

Distinct136
Distinct (%)71.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean680.96842
Minimum1
Maximum15483
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T05:30:03.287439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q117
median68
Q3314.25
95-th percentile2851.2
Maximum15483
Range15482
Interquartile range (IQR)297.25

Descriptive statistics

Standard deviation2168.7212
Coefficient of variation (CV)3.1847603
Kurtosis30.0289
Mean680.96842
Median Absolute Deviation (MAD)60.5
Skewness5.3044559
Sum129384
Variance4703351.5
MonotonicityNot monotonic
2023-12-13T05:30:03.452195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8
 
4.2%
2 7
 
3.7%
17 5
 
2.6%
11 4
 
2.1%
16 3
 
1.6%
29 3
 
1.6%
4 3
 
1.6%
12 3
 
1.6%
14 3
 
1.6%
7 3
 
1.6%
Other values (126) 148
77.9%
ValueCountFrequency (%)
1 8
4.2%
2 7
3.7%
3 2
 
1.1%
4 3
 
1.6%
5 1
 
0.5%
6 2
 
1.1%
7 3
 
1.6%
8 2
 
1.1%
9 1
 
0.5%
10 2
 
1.1%
ValueCountFrequency (%)
15483 1
0.5%
14900 1
0.5%
13948 1
0.5%
9948 1
0.5%
9368 1
0.5%
5954 1
0.5%
3665 1
0.5%
3384 1
0.5%
3047 1
0.5%
2916 1
0.5%

지휘관할_남자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct142
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean661.9
Minimum0
Maximum17725
Zeros2
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T05:30:03.609616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q119.25
median75
Q3346.75
95-th percentile2775.45
Maximum17725
Range17725
Interquartile range (IQR)327.5

Descriptive statistics

Standard deviation2107.3056
Coefficient of variation (CV)3.183722
Kurtosis37.481249
Mean661.9
Median Absolute Deviation (MAD)66.5
Skewness5.7858784
Sum125761
Variance4440737
MonotonicityNot monotonic
2023-12-13T05:30:03.763281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 6
 
3.2%
2 6
 
3.2%
1 5
 
2.6%
17 5
 
2.6%
6 3
 
1.6%
80 3
 
1.6%
66 3
 
1.6%
32 3
 
1.6%
61 3
 
1.6%
7 3
 
1.6%
Other values (132) 150
78.9%
ValueCountFrequency (%)
0 2
 
1.1%
1 5
2.6%
2 6
3.2%
3 3
1.6%
4 1
 
0.5%
5 2
 
1.1%
6 3
1.6%
7 3
1.6%
8 1
 
0.5%
9 1
 
0.5%
ValueCountFrequency (%)
17725 1
0.5%
14336 1
0.5%
12656 1
0.5%
8278 1
0.5%
7441 1
0.5%
3955 1
0.5%
3950 1
0.5%
3544 1
0.5%
2994 1
0.5%
2838 1
0.5%

지휘관할_여자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.9
Minimum0
Maximum3848
Zeros21
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T05:30:03.930766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median14
Q368
95-th percentile612.3
Maximum3848
Range3848
Interquartile range (IQR)65

Descriptive statistics

Standard deviation475.71206
Coefficient of variation (CV)3.1948426
Kurtosis35.827797
Mean148.9
Median Absolute Deviation (MAD)13
Skewness5.6471121
Sum28291
Variance226301.96
MonotonicityNot monotonic
2023-12-13T05:30:04.078060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21
 
11.1%
1 16
 
8.4%
2 10
 
5.3%
6 8
 
4.2%
3 7
 
3.7%
5 6
 
3.2%
8 5
 
2.6%
14 5
 
2.6%
4 5
 
2.6%
13 4
 
2.1%
Other values (76) 103
54.2%
ValueCountFrequency (%)
0 21
11.1%
1 16
8.4%
2 10
5.3%
3 7
 
3.7%
4 5
 
2.6%
5 6
 
3.2%
6 8
 
4.2%
7 2
 
1.1%
8 5
 
2.6%
9 1
 
0.5%
ValueCountFrequency (%)
3848 1
0.5%
3546 1
0.5%
2190 1
0.5%
2146 1
0.5%
1800 1
0.5%
1433 1
0.5%
868 1
0.5%
773 1
0.5%
698 1
0.5%
633 1
0.5%

지휘관할_미상검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1
Minimum0
Maximum331
Zeros92
Zeros (%)48.4%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T05:30:04.230228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile44.75
Maximum331
Range331
Interquartile range (IQR)4

Descriptive statistics

Standard deviation32.334814
Coefficient of variation (CV)3.5532763
Kurtosis58.915825
Mean9.1
Median Absolute Deviation (MAD)1
Skewness6.9743159
Sum1729
Variance1045.5402
MonotonicityNot monotonic
2023-12-13T05:30:04.368942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 92
48.4%
1 19
 
10.0%
3 14
 
7.4%
2 14
 
7.4%
4 8
 
4.2%
7 7
 
3.7%
5 4
 
2.1%
6 3
 
1.6%
10 3
 
1.6%
21 2
 
1.1%
Other values (21) 24
 
12.6%
ValueCountFrequency (%)
0 92
48.4%
1 19
 
10.0%
2 14
 
7.4%
3 14
 
7.4%
4 8
 
4.2%
5 4
 
2.1%
6 3
 
1.6%
7 7
 
3.7%
8 1
 
0.5%
9 2
 
1.1%
ValueCountFrequency (%)
331 1
0.5%
195 1
0.5%
145 1
0.5%
92 1
0.5%
86 1
0.5%
73 1
0.5%
66 1
0.5%
65 1
0.5%
58 1
0.5%
47 1
0.5%

지휘관할_법인(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.052632
Minimum0
Maximum593
Zeros107
Zeros (%)56.3%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T05:30:04.521679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile38
Maximum593
Range593
Interquartile range (IQR)3

Descriptive statistics

Standard deviation52.084459
Coefficient of variation (CV)4.7124034
Kurtosis91.781181
Mean11.052632
Median Absolute Deviation (MAD)0
Skewness9.0273583
Sum2100
Variance2712.7909
MonotonicityNot monotonic
2023-12-13T05:30:04.690462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 107
56.3%
1 19
 
10.0%
3 9
 
4.7%
2 9
 
4.7%
4 5
 
2.6%
7 4
 
2.1%
5 4
 
2.1%
6 3
 
1.6%
9 2
 
1.1%
28 2
 
1.1%
Other values (23) 26
 
13.7%
ValueCountFrequency (%)
0 107
56.3%
1 19
 
10.0%
2 9
 
4.7%
3 9
 
4.7%
4 5
 
2.6%
5 4
 
2.1%
6 3
 
1.6%
7 4
 
2.1%
8 1
 
0.5%
9 2
 
1.1%
ValueCountFrequency (%)
593 1
0.5%
347 1
0.5%
119 1
0.5%
109 1
0.5%
100 1
0.5%
92 1
0.5%
63 1
0.5%
42 1
0.5%
41 1
0.5%
38 2
1.1%

Interactions

2023-12-13T05:29:58.135238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:40.539182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:42.109568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:43.728834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:45.080145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:46.870187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:48.381842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:49.971520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:51.717430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:53.605589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:55.122960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:56.576510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:58.255664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:40.654982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:42.246595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:43.862305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:45.184060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:47.024578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:48.533366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:50.096050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:51.824181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:53.742790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:55.239335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:56.704387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:58.394956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:40.789176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:42.361927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:43.989634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:45.285908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:47.131446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:48.676162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:50.249402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:51.947559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:53.870330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:55.360658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:56.829347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:58.526575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:40.916102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:42.502822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:44.085495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:45.411161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:47.250310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:48.813531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:50.376550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:52.050432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:53.997848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:55.459429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:56.956254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:58.660739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:41.060234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:42.628639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:44.214820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:45.510429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:47.361128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:48.948856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:50.534686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:52.160741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:54.140695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:55.592454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:57.114239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:58.790102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:41.188934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:42.786674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:44.320745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:45.897183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:47.490073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:49.089749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:50.658579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:52.285560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:54.255612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:55.715133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:57.278470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:58.921352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:41.316149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:42.919195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:44.429713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:46.014768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:47.606466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:49.236408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:50.798165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:52.425004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:54.388523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:55.850479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:57.413130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:59.067983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:41.429666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:43.063037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:44.552466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:46.146378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:47.735136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:49.353334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:50.958809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:52.574202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:54.512049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:55.977053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:57.551255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:59.216929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:41.567534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:43.188843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:44.667003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:46.346513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:47.867189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:49.483047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:51.127893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:53.053074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:54.635626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:56.103268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:57.681340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:59.334893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:41.706270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:43.314536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:44.769761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:46.471492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:47.982436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:49.606779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:51.280618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:53.170130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:54.751050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:56.228121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:57.787543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:59.466100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:41.845057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:43.466082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:44.890949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:46.601178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:48.114065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:49.742650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:51.426208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:53.318945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:54.884395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:56.350479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:57.905632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:59.589923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:41.979739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:43.599336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:44.988224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:46.744398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:48.246047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:49.866166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:51.562738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:53.463753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:54.999788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:56.458730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:29:58.020758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:30:04.843621image/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)1.0000.9190.8890.8420.9420.6340.7220.7050.6640.5520.8930.454
직수_인지_검거건수(건)0.9191.0000.9660.9520.7510.5720.7230.5290.6870.5840.6690.755
직수_인지_남자검거인원(명)0.8890.9661.0000.9930.7020.5630.7310.6030.6760.5190.6470.727
직수_인지_여자검거인원(명)0.8420.9520.9931.0000.7010.5720.7430.6260.6840.5390.6570.755
직수_인지_미상검거인원(명)0.9420.7510.7020.7011.0000.6760.7330.7400.6580.5840.9420.477
직수_인지_법인(개)0.6340.5720.5630.5720.6761.0000.9450.7680.6910.7500.6290.921
지휘관할_발생건수(건)0.7220.7230.7310.7430.7330.9451.0000.9230.9510.9070.8510.705
지휘관할_검거건수(건)0.7050.5290.6030.6260.7400.7680.9231.0000.9010.9070.8110.640
지휘관할_남자검거인원(명)0.6640.6870.6760.6840.6580.6910.9510.9011.0000.9850.8040.556
지휘관할_여자검거인원(명)0.5520.5840.5190.5390.5840.7500.9070.9070.9851.0000.6860.663
지휘관할_미상검거인원(명)0.8930.6690.6470.6570.9420.6290.8510.8110.8040.6861.0000.452
지휘관할_법인(개)0.4540.7550.7270.7550.4770.9210.7050.6400.5560.6630.4521.000
2023-12-13T05:30:05.037707image/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)1.0000.9660.8950.6730.5850.3070.5280.5250.5790.5810.5940.189
직수_인지_검거건수(건)0.9661.0000.8700.6710.5400.3050.5350.5330.5850.5590.5520.167
직수_인지_남자검거인원(명)0.8950.8701.0000.6540.5380.3770.5420.5420.6000.5680.5980.240
직수_인지_여자검거인원(명)0.6730.6710.6541.0000.4340.3410.4570.4490.4480.5640.4410.228
직수_인지_미상검거인원(명)0.5850.5400.5380.4341.0000.3180.3340.3200.3510.3560.6720.117
직수_인지_법인(개)0.3070.3050.3770.3410.3181.0000.2370.2360.2630.2320.2760.502
지휘관할_발생건수(건)0.5280.5350.5420.4570.3340.2371.0000.9890.9510.8280.5800.317
지휘관할_검거건수(건)0.5250.5330.5420.4490.3200.2360.9891.0000.9630.8390.5530.326
지휘관할_남자검거인원(명)0.5790.5850.6000.4480.3510.2630.9510.9631.0000.8310.5990.333
지휘관할_여자검거인원(명)0.5810.5590.5680.5640.3560.2320.8280.8390.8311.0000.5980.322
지휘관할_미상검거인원(명)0.5940.5520.5980.4410.6720.2760.5800.5530.5990.5981.0000.348
지휘관할_법인(개)0.1890.1670.2400.2280.1170.5020.3170.3260.3330.3220.3481.000

Missing values

2023-12-13T05:30:00.097198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:30:00.345407image/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)
0절도17132234013703936874412190270
1불법사용010000423653000
2침입절도2220004954352392200
3장물5430201421293134250
4사기75038869916213525191811548314336354633163
5컴퓨터등사용사기4431003561181072920
6부당이득000000322000
7편의시설부정이용00000012164611433
8전기통신금융사기피해금환급에관한특별법010000251111923910
9보험사기방지특별법21000099881234400
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)
180통신비밀보호법110110979110
181특가법(도주차량)0000004894314416940
182특허법000000151922417
183폐기물관리법00600012311214314538
184풍속영업의 규제에 관한법률0001003030321100
185학교보건법000000776100
186학원의설립운영 및 과외교습에 관한법률0000003332151900
187화물자동차 운수사업법0000003363283581316
188화학물질관리법223003232432505
189기타특별법11194125182241625859543950180066347